BI Performance Metrics to Scrutinize Your Business Strategy

BI Performance Metrics to Scrutinize Your Business Strategy
The top-performing companies use data to navigate their way, and there's absolutely no reason why you shouldn't do the same with your business choices. Key performance indicators (KPIs) in business intelligence enable you to get insight into the overall health of your organization, any of your departments, or even how your consumers view your company. And you don't have to play the BI game manually anymore. You only need to invest in a good business intelligence platform to weave the magic figures for you, a feat that was formerly extremely constrained or even forbidden. If you are knowledgeable enough, you should be able to determine which business intelligence tools are best suited to your particular requirements.

In this article, we will provide some of the most important business intelligence key performance indicators (KPIs) to give you a head start in analyzing how your company aligns with the objectives you have set for it at any point in time.

Business Intelligence Key Performance Indicators for Evaluating Business Strategies


Financial Metrics

The most important metric of all. To calculate your financial metrics, you must look at your cash flow, balance sheet, and income statement using a tool such as your accounting software. Any of these criteria should tell you whether your company is financially sound, which indicates it is producing income and managing its finances well. If you want to push your company on a new growth path or spark the attention of possible investors, you will offer them these financial KPIs as evidence of investment value.

Example:
  • Liquidity Ratio
  • Net Income vs. Net Earnings
  • Working Capital
  • Debt to Equity Ratio

Marketing Metrics

In terms of business effectiveness, marketing metrics rank second only to financial metrics. Marketing metrics show the data that tells you if your most recent marketing initiatives are achieving the results you expected. Capable marketing software tools must provide you with values, such as your new content strategy riding your most recent marketing initiatives across numerous platforms.

Example:
  • Customer Acquisition Cost (CAC)
  • Conversion Rate
  • Average Spend Per Customer


Project Management Metrics

If your finances and marketing expenses are in order, it could simply be because your production departments are working hard to complete projects on time or ahead of schedule, under budget, and while keeping both clients and employees satisfied.

But what should be measured to know where everything moved at the end of the day or year? As a company owner or business leader, you should consider your productivity, profit margins, ROI, customer satisfaction, and earned value, among other things—all of which can be easily obtained from any of the top project management systems.

Example:
  • Return on Investment (ROI)
  • Productivity


Customer Service Metrics

When you consider that 89% of U.S. consumers have moved to a company competitor after a bad experience, it is evident that any customer service metrics have to go beyond operational information and include how your customer service team engages with your customers. Experience data acknowledges the personal human aspect at the heart of company and customer relationships, enabling you to discover how your consumers value their interactions with your customer service executives.

Example:
  • Customer Effort Score (CES)
  • Net Promoter Score (NPS)


Closing Lines

Business intelligence is an essential technology that provides crucial information about a company's operations. Identifying which aspects of your organization are performing well is one thing; ensuring that they continue to do so while addressing those that are struggling to stay up and meet your aims is another. Business intelligence solutions allow you to assess performance, identify shortcomings, and develop plans to increase workflow effectiveness and customer engagement.

 

Spotlight

Data Science Council of America

The Data Science Council of America (DASCA) is an independent, third–party, international credentialing organization for Big Data professionals. DASCA credentialing programs for aspiring and working big data professionals are fleshed on the world's first vendor–neutral standards – the five–pronged DASCA Essential Knowledge Framework (EKF™). The EKF™ enunciates for both big data professionals and recruiters alike, a universal and standardized model of Essential Knowledge Prerequisites, which DASCA concludes as critical for big data professionals to possess if they desire to excel consistently in their jobs and establish themselves in the league of world's finest big data professionals.

OTHER ARTICLES
Business Intelligence, Big Data Management, Big Data

How to Overcome Challenges in Adopting Data Analytics

Article | April 27, 2023

Achieving organizational success and making data-driven decisions in 2020 requires embracing tech tools like Data Analytics and collecting, storing and analysing data isn’t.The real data-driven, measurable growth, and development come with the establishment of data-driven company culture.In this type of culture company actively uses data resources as a primary asset to make smart decisions and ensure future growth. Despite the rapid growth of analytic solutions, a recent Gartner survey revealed that almost 75% of organizations thought their analytics maturity had not reached a level that optimized business outcomes. Just like with any endeavor, your organization must have a planned strategy to achieve its analytical goals. Let’s explore ways for overcoming common blockers, and elements used in successful analytics adoption strategies. Table of Contents: - AMM: Analytic Maturity Model - What are the blockers to achieving a strategy-driven analytics? - What are the adoption strategies to achieve an analytics success? - Conclusion AMM: Analytic Maturity Model The Analytic Maturity Model (AMM) evaluates the analytic maturity of an organization.The model identifies the five stages an organization travels through to reach optimization. Organizations must implement the right tools, engage their team in proper training, and provide the management support necessary to generate predictable outcomes with their analytics. Based on the maturity of these processes, the AMM divides organizations into five maturity levels: - Organizations that can build reports. - Organizations that can build and deploy models. - Organizations that have repeatable processes for building and deploying analytics. - Organizations that have consistent enterprise-wide processes for analytics. - Enterprises whose analytics is strategy driven. READ MORE:EFFECTIVE STRATEGIES TO DEMOCRATIZE DATA SCIENCE IN YOUR ORGANIZATION What are the blockers to achieving a strategy-driven analytics? - Missing an Analytics Strategy - Analytics is not for everyone - Data quality presents unique challenges - Siloed Data - Changing the culture What are the adoption strategies to achieve analytic success? • Have you got a plan to achieve analytic success? The strategy begins with business intelligence and moves toward advanced analytics. The approach differs based on the AMM level. The plan may address the strategy for a single year, or it may span 3 or more years. It ideally has milestones for what the team will do. When forming an analytics strategy, it can be expensive and time consuming at the outset. While organizations are encouraged to seek projects that can generate quick wins, the truth is that it may be months before any actionable results are available. During this period, the management team is frantically diverting resources from other high-profile projects. If funds are tight, this situation alone may cause friction. It may not be apparent to everyone how the changes are expected to help. Here are the elements of a successful analytics strategy: • Keep the focus tied to tangible business outcomes The strategy must support business goals first. With as few words as possible, your plan should outline what you intend to achieve, how to complete it, and a target date for completion of the plan. Companies may fail at this step because they mistake implementing a tool for having a strategy. To keep it relevant, tie it to customer-focused goals. The strategy must dig below the surface with the questions that it asks. Instead of asking surface questions such as “How can we save money?”, instead ask, “How can we improve the quality of the outcomes for our customers?” or “What would improve the productivity of each worker?” These questions are more specific and will get the results the business wants. You may need to use actual business cases from your organization to think through the questions. • Select modern, multi-purpose tools The organization should be looking for an enterprise tool that supports integrating data from various databases, spreadsheets, or even external web based sources. Typically, organizations may have their data stored across multiple databases such as Salesforce, Oracle, and even Microsoft Access. The organization can move ahead quicker when access to the relevant data is in a single repository. With the data combined, the analysts have a specific location to find reports and dashboards. The interface needs to be robust enough to show the data from multiple points of view. It should also allow future enhancements, such as when the organization makes the jump into data science. Incorta’s Data Analytics platform simplifies and processes data to provide meaningful information at speed that helps make informed decisions. Incorta is special in that it allows business users to ask the same complex and meaningful questions of their data that typically require many IT people and data scientist to get the answers they need to improve their line of business. At the digital pace of business today, that can mean millions of dollars for business leaders in finance, supply chain or even marketing. Speed is a key differentiator for Incorta in that rarely has anyone been able to query billions of rows of data in seconds for a line of business owner. - Tara Ryan, CMO, Incorta Technology implementations take time. That should not stop you from starting in small areas of the company to look for quick wins. Typically, the customer-facing processes have areas where it is easier to collect data and show opportunities for improvement. • Ensure staff readiness If your current organization is not data literate, then you will need resources who understand how to analyze and use data for process improvement. It is possible that you can make data available and the workers still not realize what they can do with it. The senior leadership may also need training about how to use data and what data analytics makes possible. • Start Small to Control Costs and Show Potential If the leadership team questions the expense, consider doing a proof of concept that focuses on the tools and data being integrated quickly and efficiently to show measurable success. The business may favor specific projects or initiatives to move the company forward over long-term enterprise transformations (Bean & Davenport, 2019). Keeping the project goals precise and directed helps control costs and improve the business. As said earlier, the strategy needs to answer deeper business questions. Consider other ways to introduce analytics into the business. Use initiatives that target smaller areas of the company to build competencies. Provide an analytics sandbox with access to tools and training to encourage other non-analytics workers (or citizen data scientists) to play with the data. One company formed a SWAT team, including individuals from across the organization. The smaller team with various domain experience was better able to drive results. There are also other approaches to use – the key is to show immediate and desirable results that align with organizational goals. • Treating the poor data quality What can you do about poor data quality at your company? Several solutions that can help to improve productivity and reduce the financial impact of poor data quality in your organization include: • Create a team to set the proper objectives Create a team who owns the data quality process. This is important to prove to yourself and to anyone with whom you are conversing about data that you are serious about data quality. The size of the team is not as important as the membership from the parts of the organization that have the right impact and knowledge in the process. When the team is set, make sure that they create a set of goals and objectives for data quality. To gauge performance, you need a set of metrics to measure the performance. After you create the proper team to govern your data quality, ensure that the team focuses on the data you need first. Everyone knows the rules of "good data in, good data out" and "bad data in, bad data out." To put this to work, make sure that your team knows the relevant business questions that are in progress across various data projects to make sure that they focus on the data that supports those business questions. • Focus on the data you need now as the highest priority Once you do that, you can look at the potential data quality issues associated with each of the relevant downstream business questions and put the proper processes and data quality routines in place to ensure that poor data quality has a low probability of Successful Analytics Adoption Strategies, continuing to affect that data. As you decide which data to focus on, remember that the key for innovators across industries is that the size of the data isn’t the most critical factor — having the right data is (Wessel, 2016). • Automate the process of data quality when data volumes grow too large When data volumes become unwieldy and difficult to manage the quality, automate the process. Many data quality tools in the market do a good job of removing the manual effort from the process. Open source options include Talend and DataCleaner. Commercial products include offerings from DataFlux, Informatica, Alteryx and Software AG. As you search for the right tool for you and your team, beware that although the tools help with the organization and automation, the right processes and knowledge of your company's data are paramount to success. • Make the process of data quality repeatable It needs regular care and feeding. Remember that the process is not a one-time activity. It needs regular care and feeding. While good data quality can save you a lot of time, energy, and money downstream, it does take time, investment, and practice to do well. As you improve the quality of your data and the processes around that quality, you will want to look for other opportunities to avoid data quality mishaps. • Beware of data that lives in separate databases When data is stored in different databases, there can be issues with different terms being used for the same subject. The good news is that if you have followed the former solutions, you should have more time to invest in looking for the best cases. As always, look for the opportunities with the biggest bang for the buck first. You don't want to be answering questions from the steering committee about why you are looking for differences between "HR" and "Hr" if you haven't solved bigger issues like knowing the difference between "Human Resources" and "Resources," for example. • De-Siloing Data The solution to removing data silos typically isn’t some neatly packaged, off-the-shelf product. Attempts to quickly create a data lake by simply pouring all the siloed data together can result in an unusable mess, turning more into a data swamp. This is a process that must be done carefully to avoid confusion, liability, and error. Try to identify high-value opportunities and find the various data stores required to execute those projects. Working with various business groups to find business problems that are well-suited to data science solutions and then gathering the necessary data from the various data stores can lead to high-visibility successes. As value is proved from joining disparate data sources together to create new insights, it will be easier to get buy-in from upper levels to invest time and money into consolidating key data stores. In the first efforts, getting data from different areas may be akin to pulling teeth, but as with most things in life, the more you do it, the easier it gets. Once the wheels get moving on a few of these integration projects, make wide-scale integration the new focus. Many organizations at this stage appoint a Chief Analytics Officer (CAO) who helps increase collaboration between the IT and business units ensuring their priorities are aligned. As you work to integrate the data, make sure that you don’t inadvertently create a new “analytics silo.” The final aim here is an integrated platform for your enterprise data. • Education is essential When nearly 45% of workers generally prefer status quo over innovation, how do you encourage an organization to move forward? If the workers are not engaged or see the program as merely just the latest management trend, it may be tricky to convince them. Larger organizations may have a culture that is slow to change due to their size or outside forces. There’s also a culture shift required - moving from experience and knee-jerk reactions to immersion and exploration of rich insights and situational awareness. - Walter Storm, the Chief Data Scientist, Lockheed Martin Companies spend a year talking about an approved analytics tool before moving forward. The employees had time to consider the change and to understand the new skill sets needed. Once the entire team embraced the change, the organization moved forward swiftly to convert existing data and reports into the new tool. In the end, the corporation is more successful, and the employees are still in alignment with the corporate strategy. If using data to support decisions is a foreign concept to the organization, it’s a smart idea to ensure the managers and workers have similar training. This training may involve everything from basic data literacy to selecting the right data for management presentations. However, it cannot stop at the training; the leaders must then ask for the data to move forward with requests that will support conclusions that will be used to make critical decisions across the business. These methods make it easier to sell the idea and keep the organization’s analytic strategy moving forward. Once senior leadership uses data to make decisions, everyone else will follow their lead. It is that simple. Conclusion The analytics maturity model serves as a useful framework for understanding where your organization currently stands regarding strategy, progress, and skill sets. Advancing along the various levels of the model will become increasingly imperative as early adopters of advanced analytics gain a competitive edge in their respective industries. Delay or failure to design and incorporate a clearly defined analytics strategy into an organization’s existing plan will likely result in a significant missed opportunity. READ MORE:BIG DATA ANALYTICS STRATEGIES ARE MATURING QUICKLY IN HEALTHCARE

Read More
Business Intelligence, Enterprise Business Intelligence

Data Virtualization: A Dive into the Virtual Data Lake

Article | July 10, 2023

No matter if you own a retail business, a financial services company, or an online advertising business, data is the most essential resource for contemporary businesses. Businesses are becoming more aware of the significance of their data for business analytics, machine learning, and artificial intelligence across all industries. Smart companies are investing in innovative approaches to derive value from their data, with the goals of gaining a deeper understanding of the requirements and actions of their customers, developing more personalized goods and services, and making strategic choices that will provide them with a competitive advantage in the years to come. Business data warehouses have been utilized for all kinds of business analytics for many decades, and there is a rich ecosystem that revolves around SQL and relational databases. Now, a competitor has entered the picture. Data lakes were developed for the purpose of storing large amounts of data to be used in the training of AI models and predictive analytics. For most businesses, a data lake is an essential component of any digital transformation strategy. However, getting data ready and accessible for creating insights in a controllable manner remains one of the most complicated, expensive, and time-consuming procedures. While data lakes have been around for a long time, new tools and technologies are emerging, and a new set of capabilities are being introduced to data lakes to make them more cost-effective and more widely used. Why Should Businesses Opt for Virtual Data Lakes and Data Virtualization? Data virtualization provides a novel approach to data lakes; modern enterprises have begun to use logical data lake architecture, which is a blended method based on a physical data lake but includes a virtual data layer to create a virtual data lake. Data virtualization combines data from several sources, locations, and formats without requiring replication. In a process that gives many applications and users unified data services, a single "virtual" data layer is created. There are many reasons and benefits for adding a virtual data lake and data virtualization, but we will have a look at the top three reasons that will benefit your business. Reduced Infrastructure Costs Database virtualization can save you money by eliminating the need for additional servers, operating systems, electricity, application licensing, network switches, tools, and storage. Lower Labor Costs Database virtualization makes the work of a database IT administrator considerably easier by simplifying the backup process and enabling them to handle several databases at once. Data Quality Marketers are nervous about the quality and accuracy of the data that they have. According to Singular, in 2019, 13% responded that accuracy was their top concern. And 12% reported having too much data. Database virtualization improves data quality by eliminating replication. Virtual Data Lake and Marketing Leaders Customer data is both challenging as well as an opportunity for marketers. If your company depends on data-driven marketing on any scale and expects to retain a competitive edge, there is no other option: it is time to invest in a virtual data lake. In the omnichannel era, identity resolution is critical to consumer data management. Without it, business marketers would be unable to develop compelling customer experiences. Marketers could be wondering, "A data what?" Consider data lakes in this manner: They provide marketers with important information about the consumer journey as well as immediate responses about marketing performance across various channels and platforms. Most marketers lack insight into performance because they lack the time and technology to filter through all of the sources of that information. A virtual data lake is one solution. Marketers can reliably answer basic questions like, "How are customers engaging with our goods and services, and where is that occurring in the customer journey?" using a data lake. "At what point do our conversion rates begin to decline?" The capacity to detect and solve these sorts of errors at scale and speed—with precise attribution and without double-counting—is invaluable. Marketers can also use data lakes to develop appropriate standards and get background knowledge of activity performance. This provides insight into marketing ROI and acts as a resource for any future marketing initiatives and activities. Empowering Customer Data Platform Using Data Virtualization Businesses are concentrating more than ever on their online operations, which means they are spending more on digital transformation. This involves concentrating on "The Customer," their requirements and insights. Customers have a choice; switching is simple, and customer loyalty is inexpensive, making it even more crucial to know your customer and satisfy their requirements. Data virtualization implies that the customer data platform (CDP) serves as a single data layer that is abstracted from the data source's data format or schemas. The CDP offers just the data selected by the user with no bulk data duplication. This eliminates the need for a data integrator to put up a predetermined schema or fixed field mappings for various event types. Retail Businesses are Leveraging Data Virtualization Retailers have been servicing an increasingly unpredictable customer base over the last two decades. They have the ability to do research, check ratings, compare notes among their personal and professional networks, and switch brands. They now expect to connect with retail businesses in the same way that they interact with social networks. To accomplish so, both established as well as modern retail businesses must use hybrid strategies that combine physical and virtual businesses. In order to achieve this, retail businesses are taking the help of data virtualization to provide seamless experiences across online and in-store environments. How Does Data Virtualization Help in the Elimination of Data Silos? To address these data-silo challenges, several businesses are adopting a much more advanced data integration strategy: data virtualization. In reality, data virtualization and data lakes overlap in many aspects. Both architectures start with the assumption that all data should be accessible to end users. Broad access to big data volumes is employed in both systems to better enable BI and analytics as well as other emerging trends like artificial intelligence and machine learning. Data Virtualization can address a number of big data pain points with features such as query pushdown, caching, and query optimization. Data virtualization enables businesses to access data from various sources such as data warehouses, NoSQL databases, and data lakes without requiring physical data transportation thanks to a virtual layer that covers the complexities of source data from the end user. A couple of use cases where data virtualization can eliminate data silos are: Agile Business Intelligence Legacy BI solutions are now unable to meet the rising enterprise BI requirements. Businesses now need to compete more aggressively. As a result, they must improve the agility of their processes. Data virtualization can improve system agility by integrating data on-demand. Moreover, it offers uniform access to data in a unified layer that can be merged, processed, and cleaned. Businesses may also employ data virtualization to build consistent BI reports for analysis with reduced data structures and instantly provide insights to key decision-makers. Virtual Operational Data Store The Virtual Operational Data Store (VODS) is another noteworthy use of data virtualization. Users can utilize VODS to execute additional operations on the data analyzed by data virtualization, like monitoring, reporting, and control. GPS applications are a perfect example of VODS. Travelers can utilize these applications to get the shortest route to a certain location. A VODS takes data from a variety of data repositories and generates reports on the fly. So, the traveler gets information from a variety of sources without having to worry about which one is the main source. Closing Lines Data warehouses and virtual data lakes are both effective methods for controlling huge amounts of data and advancing to advanced ML analytics. Virtual data lakes are a relatively new technique for storing massive amounts of data on commercial clouds like Amazon S3 and Azure Blob. While dealing with ML workloads, the capacity of a virtual data lake and data virtualization to harness more data from diverse sources in much less time is what makes it a preferable solution. It not only allows users to cooperate and analyze data in new ways, but it also accelerates decision-making. When you require business-friendly and well-engineered data displays for your customers, it makes a strong business case. Through data virtualization, IT can swiftly deploy and repeat a new data set as client needs change. When you need real-time information or want to federate data from numerous sources, data virtualization can let you connect to it rapidly and provide it fresh each time. Frequently Asked Questions What Exactly Is a “Virtual Data Lake?” A virtual data lake is connected to or disconnected from data sources as required by the applications that are using it. It stores data summaries in the sources such that applications can explore the data as if it were a single data collection and obtain entire items as required. What Is the Difference Between a Data Hub and a Data Lake? Data Lakes and Data Hubs (Datahub) are two types of storage systems. A data lake is a collection of raw data that is primarily unstructured. On the other hand, a data hub, is made up of a central storage system whose data is distributed throughout several areas in a star architecture. Does Data Virtualization Store Data? It is critical to understand that data virtualization doesn't at all replicate data from source systems; rather, it saves metadata and integration logic for viewing.

Read More
Business Intelligence, Big Data Management, Data Science

Big Data in Healthcare: Improving Patient Outcomes

Article | May 2, 2023

Explore the impact of big data on the healthcare industry and how it is being used to improve patient outcomes. Discover how big data is being leveraged to enhance overall healthcare delivery. Contents 1. Introduction 1.1 Role of Big Data in Healthcare 1.2 The Importance of Patient Outcomes 2. How Big Data Improves Patient Outcomes 2.1 Personalized Medicine and Treatment Plans 2.2 Early Disease Detection and Prevention 2.3 Improved Patient Safety and Reduced Medical Errors 3. Challenges and Considerations While Using Big Data in Healthcare 4. Final thoughts 1. Introduction In today's constantly evolving healthcare industry, the significance of big data cannot be overstated. Its multifaceted nature makes it a valuable asset to healthcare providers in their efforts to enhance patient outcomes and reduce business costs. When harnessed effectively, big data in healthcare provides companies with the insights they need to personalize healthcare, streamline customer service processes, and improve their practices for interacting with patients. This results in a more tailored and thorough experience for customers, ultimately leading to better care. 1.1 Role of Big Data in Healthcare Big data pertains to vast collections of structured and unstructured data in the healthcare industry. One of the primary sources of big data in healthcare is electronic health records (EHRs), which contain: Patient’s medical history Demographics Medications Test results Analyzing this data can: Facilitate informed decision-making Improve patient outcomes Reduce healthcare costs Integrating structured and unstructured data can add significant value to healthcare organizations, and Big Data Analytics (BDA) is the tool used to extract information from big data. Big Data Analytics (BDA) can extract information and create trends, and in healthcare, it can identify clusters, correlations, and predictive models from large datasets. However, privacy and security concerns and ensuring data accuracy and reliability are significant challenges that must be addressed. 1.2 The Importance of Patient Outcomes Patient outcomes are the consequences of healthcare interventions or treatments on a patient's health status and are essential in evaluating healthcare systems and guiding healthcare decision-making. However, the current healthcare system's focus on volume rather than value has led to fragmented payment and delivery systems that fall short in terms of quality, outcomes, costs, and equity. To overcome these shortcomings, a learning healthcare system is necessary to continuously apply knowledge for improved patient outcomes and affordability. However, access to timely guidance is limited, and organizational and technological limitations pose significant challenges in measuring patient-centered outcomes. 2. How Big Data Improves Patient Outcomes Big data in healthcare engenders a substantial impact by facilitating the delivery of treatment that is both efficient and effective. This innovative approach to healthcare enables the identification of high-risk patients, prediction of disease outbreaks, management of hospital performance, and improvement of treatment effectiveness. Thanks to modern technology, the collection of electronic data is now a seamless process, thus empowering healthcare professionals to create data-driven solutions to improve patient outcomes. 2.1 Personalized Medicine and Treatment Plans Big data can revolutionize personalized medicine and treatment plans by analyzing vast patient data to create tailored treatment plans for each patient, resulting in better outcomes, fewer side effects, and faster recovery times. 2.2 Early Disease Detection and Prevention Big data analytics in healthcare allow for early interventions and treatments by identifying patterns and trends that indicate disease onset. This improves patient outcomes and reduces healthcare costs. Real-time patient data monitoring and predictive analytics enable timely action to prevent complications. 2.3 Improved Patient Safety and Reduced Medical Errors Big data analytics can help healthcare providers identify safety risks like medication errors, misdiagnoses, and adverse reactions, improving patient safety and reducing medical errors. This can lead to cost savings and better patient outcomes. 3. Challenges and Considerations While Using Big Data in Healthcare In order to maximize the potential advantages, organizations must address significant challenges of big data in healthcare, like privacy and security concerns, data accuracy and reliability, and expertise and technology requirements. Safeguards like encryption, access controls, and data de-identification can mitigate privacy and security risks Ensuring data accuracy and reliability requires standardized data collection, cleaning, and validation procedures Additionally, healthcare organizations must prioritize the recruitment of qualified professionals with expertise in data management, and analysis is crucial The adoption of advanced technologies such as artificial intelligence and machine learning can support effective analysis and interpretation of big data in healthcare 4. Final Thoughts The impact of big data on healthcare is profound, and the healthcare sector possesses the possibility of a paradigm shift by leveraging the potential of big data to augment patient outcomes and curtail costs. Nevertheless, implementing big data entails formidable challenges that necessitate their resolution to fully unleash healthcare data technology's benefits. Notably, handling voluminous and heterogeneous datasets in real time requires state-of-the-art technological solutions. To attain the maximal benefits of big data in healthcare, organizations must proactively address these challenges by implementing risk-mitigating measures and fully capitalizing on big data's potential.

Read More
Business Intelligence

Embedded Business Intelligence- A Guide to an Upgraded BI

Article | April 12, 2022

Businesses are becoming more data-driven, and the potential to use data and analytics to differentiate market leaders is becoming increasingly important. Customers are demanding actionable insights into the apps, products, and services they use daily, and businesses of all sizes are trying to meet these demands. Product managers understand they must provide their consumers with concrete insights derived from processed data. However, creating these features from scratch can sometimes be a difficult task. The answer is simple: add an analytics platform into your core product, like integrated business intelligence. Embedding an analytics system may also help a company get more value out of the data it has already spent time acquiring, keeping, and analyzing. Embedded business intelligence is among the most important use cases in the broader data analytics sector, as companies leverage the technology to build extranet apps and give analytics as part of a larger business application. Those looking to integrate analytics tools into their existing business operations must prioritize their requirements in order of importance. Why Should Businesses Choose Embedded Business Intelligence? Embedded business intelligence (Embedded BI) is the future of BI, because it makes it easy for your employees to use dashboards and make data-based decisions as they go about their work. Let's look at some of the reasons why you should opt for embedded business intelligence. Insightful Decision-Making Embedding BI allows you to leverage insights, making data more accessible irrespective of technical skills. Embedded analytics tools provide you with quick access to data that can help you make better business decisions. If “glitches” show up on the radar, strategic decision-makers can raise the alarm, assess the threat, develop remedies, and come up with solutions, and change the business course. Create an Effortless Workflow According to MarTech Today and Blissfully, "businesses with fewer than 50 employees have approximately 40 applications in total." The truth is that current employee operations are complicated and scattered across several platforms. BI platforms aren't a silver bullet for this challenge. Embedded BI, on the other hand, can be beneficial. Embedded BI eliminates the need for your sales executive to make choices and streamlines their workflow. It seamlessly integrates the data into this team's existing tool process with minimal disruption. Reduce your Reliance on Developers Businesses that depend entirely on their overburdened developers to implement an analytics solution will invariably create a data bottleneck. Embedded BI tools reduce this barrier and encourages everyone who works with embedded data to be more flexible and iterative. With the help of embedded business intelligence, you can check and analyze business data and adjust visuals on the go by utilizing dynamic data visualization. Drill-down, filtering, and search are interaction options available on these embedded BI tools, allowing to freely explore reports and dashboards and extract crucial business insights. Should You Build In-House Embedded BI or Buy a Third-Party? When it comes to deploying an embedded BI tool, you have two options. Organizations can either develop their products in-house or buy them from a third party. Building an embedded BI platform from scratch might take a long time and may be costly like most businesses with software as their key competence, general companies should first explore commercially available embedded BI solutions. Also, purchasing embedded BI allows businesses to focus on their core competencies while leveraging the tools to deliver embedded BI features to users faster. Top Embedded Business Intelligence Tools for C-Suite (Include cases) Many embedded BI tools are available in the market but choosing the most appropriate tool from among them is a major task. So, to end your search for the perfect embedded BI tool, you can check out the list below. We have also included case studies of these embedded business intelligence applications for you to make a better decision. Sisense BI Helps Crunchbase Get Access to the Right Data across the Organization In the business world, Crunchbase is the most important database, and they needed a powerful platform to get all their data together, so they went with Sisense BI. Crunchbase was able to take its analytics to the next level using Sisense for Cloud Data Teams, which allowed them to access their data, from their marketing stack to Salesforce platforms to website impression data, to create a holistic view of their business and customers. It's also good for Crunchbase's marketing team because the interface of Sisense is easy to use. This makes it easy for business users to understand data on their own and use it for decision making. Microsoft Power BI Helps Heathrow Airport in Making Travels Less Stressful Heathrow Airport serves as the U.K.'s international gateway. Heathrow Airport serves 80 million passengers each day, and the airport is utilizing Microsoft Power BI and Microsoft Azure to make travel less stressful for travelers. With the help of Power BI, Heathrow Airport gets real-time operational data for its employees. It enables to assist passengers in navigating the airport despite bad weather, canceled flights, and other delays. For example, a disturbance in the jet stream caused a delay of 20 flights, resulting in 6,000 more passengers arriving at the airport at 6:00 p.m. Previously, employees at immigration, customs, luggage handling, and food services would not be aware of the unexpected passengers until they arrived, forcing them to make do with what they had. But now, all these employees are notified one to two hours prior so that they can arrange extra workers, buses, food, and other resources to assist with the inflow. Qlik Sense Helps Tesla Users Get Information About Tesla SuperCharge Stations Tesla customers use a Qlik Sense application to track the locations of Tesla supercharger stations and obtain information about them. The software uses real-world road network computations and overlap predictions based on Tesla vehicles' typical battery range. This app needs to work with Qlik GeoAnalytics because it displays supercharging stations on a map. Charger status is also displayed on the dashboard. You can make choices based on where you are on the dashboard, and the program will respond based on the associations between data sets. Closing Lines Embedded business intelligence has significant potential for small firms and enterprise powerhouses alike. Embedded analytics outperforms previous solutions in extracting the most value from your data and enabling today's crucial business choices. However, long-term use of embedded analytics will require a significant amount of work on the part of the C-suite. The C-suite will have a positive influence and assure continued analytics success by applying predictive analytics, integrating machine learning, and encouraging a data-driven culture. FAQ Is there a limit to embedding analytics into existing applications? Embedded BI products have less limitations than independent tools and are mostly more capable. Machine learning, NLP, and artificial intelligence (AI) are included in the current, more modern generation of embedded systems, although these abilities are generally not included in standalone solutions. What should purchasers keep in mind while selecting a vendor? Users who have only used a typical BI or data analytics tool should be wary of colorful charts and data visualizations. Buyers must think about the long term, particularly when it comes to product maintenance, making changes across instances, and offering a simple yet tailored experience to the end-user. Are embedded business intelligence solutions easy to set up? The beauty of embedded analytics and BI solutions is quick and simple to deploy. You can either add them to an existing system or design a new one based on your requirements.

Read More

Spotlight

Data Science Council of America

The Data Science Council of America (DASCA) is an independent, third–party, international credentialing organization for Big Data professionals. DASCA credentialing programs for aspiring and working big data professionals are fleshed on the world's first vendor–neutral standards – the five–pronged DASCA Essential Knowledge Framework (EKF™). The EKF™ enunciates for both big data professionals and recruiters alike, a universal and standardized model of Essential Knowledge Prerequisites, which DASCA concludes as critical for big data professionals to possess if they desire to excel consistently in their jobs and establish themselves in the league of world's finest big data professionals.

Related News

Data Science

Snowflake Accelerates How Users Build Next Generation Apps and Machine Learning Models in the Data Cloud

Business Wire | November 03, 2023

Snowflake (NYSE: SNOW), the Data Cloud company, today announced at its Snowday 2023 event new advancements that make it easier for developers to build machine learning (ML) models and full-stack apps in the Data Cloud. Snowflake is enhancing its Python capabilities through Snowpark to boost productivity, increase collaboration, and ultimately speed up end-to-end AI and ML workflows. In addition, with support for containerized workloads and expanded DevOps capabilities, developers can now accelerate development and run apps — all within Snowflake's secure and fully managed infrastructure. “The rise of generative AI has made organizations’ most valuable asset, their data, even more indispensable. Snowflake is making it easier for developers to put that data to work so they can build powerful end-to-end machine learning models and full-stack apps natively in the Data Cloud,” said Prasanna Krishnan, Senior Director of Product Management, Snowflake. “With Snowflake Marketplace as the first cross-cloud marketplace for data and apps in the industry, customers can quickly and securely productionize what they’ve built to global end users, unlocking increased monetization, discoverability, and usage.” Developers Gain Robust and Familiar Functionality for End-to-End Machine Learning Snowflake is continuing to invest in Snowpark as its secure deployment and processing of non-SQL code, with over 35% of Snowflake customers using Snowpark on a weekly basis (as of September 2023). Developers increasingly look to Snowpark for complex ML model development and deployment, and Snowflake is introducing expanded functionality that makes Snowpark even more accessible and powerful for all Python developers. New advancements include: Snowflake Notebooks (private preview): Snowflake Notebooks are a new development interface that offers an interactive, cell-based programming environment for Python and SQL users to explore, process, and experiment with data in Snowpark. Snowflake’s built-in notebooks allow developers to write and execute code, train and deploy models using Snowpark ML, visualize results with Streamlit chart elements, and much more — all within Snowflake’s unified, secure platform. Snowpark ML Modeling API (general availability soon): Snowflake’s Snowpark ML Modeling API empowers developers and data scientists to scale out feature engineering and simplify model training for faster and more intuitive model development in Snowflake. Users can implement popular AI and ML frameworks natively on data in Snowflake, without having to create stored procedures. Snowpark ML Operations Enhancements: The Snowpark Model Registry (public preview soon) now builds on a native Snowflake model entity and enables the scalable, secure deployment and management of models in Snowflake, including expanded support for deep learning models and open source large language models (LLMs) from Hugging Face. Snowflake is also providing developers with an integrated Snowflake Feature Store (private preview) that creates, stores, manages, and serves ML features for model training and inference. Endeavor, the global sports and entertainment company that includes the WME Agency, IMG & On Location, UFC, and more, relies on Snowflake’s Snowpark for Python capabilities to build and deploy ML models that create highly personalized experiences and apps for fan engagement. Snowpark serves as the driving force behind our end-to-end machine learning development, powering how we centralize and process data across our various entities, and then securely build and train models using that data to create hyper-personalized fan experiences at scale, said Saad Zaheer, VP of Data Science and Engineering, Endeavor. With Snowflake as our central data foundation bringing all of this development directly to our enterprise data, we can unlock even more ways to predict and forecast customer behavior to fuel our targeted sales and marketing engines. Snowflake Advances Developer Capabilities Across the App Lifecycle The Snowflake Native App Framework (general availability soon on AWS, public preview soon on Azure) now provides every organization with the necessary building blocks for app development, including distribution, operation, and monetization within Snowflake’s platform. Leading organizations are monetizing their Snowflake Native Apps through Snowflake Marketplace, with app listings more than doubling since Snowflake Summit 2023. This number is only growing as Snowflake continues to advance its developer capabilities across the app lifecycle so more organizations can unlock business impact. For example, Cybersyn, a data-service provider, is developing Snowflake Native Apps exclusively for Snowflake Marketplace, with more than 40 customers running over 5,000 queries with its Financial & Economic Essentials Native App since June 2022. In addition, LiveRamp, a data collaboration platform, has seen the number of customers deploying its Identity Resolution and Transcoding Snowflake Native App through Snowflake Marketplace increase by more than 80% since June 2022. Lastly, SNP has been able to provide its customers with a 10x cost reduction in Snowflake data processing associated with SAP data ingestion, empowering them to drastically reduce data latency while improving SAP data availability in Snowflake through SNP’s Data Streaming for SAP - Snowflake Native App. With Snowpark Container Services (public preview soon in select AWS regions), developers can run any component of their app — from ML training, to LLMs, to an API, and more — without needing to move data or manage complex container-based infrastructure. Snowflake Automates DevOps for Apps, Data Pipelines, and Other Development Snowflake is giving developers new ways to automate key DevOps and observability capabilities across testing, deploying, monitoring, and operating their apps and data pipelines — so they can take them from idea to production faster. With Snowflake’s new Database Change Management (private preview soon) features, developers can code declaratively and easily templatize their work to manage Snowflake objects across multiple environments. The Database Change Management features serve as a single source of truth for object creation across various environments, using the common “configuration as code” pattern in DevOps to automatically provision and update Snowflake objects. Snowflake also unveiled a new Powered by Snowflake Funding Program, innovations that enable all users to securely tap into the power of generative AI with their enterprise data, enhancements to further eliminate data silos and strengthen Snowflake’s leading compliance and governance capabilities through Snowflake Horizon, and more at Snowday 2023.

Read More

Business Intelligence

Wiiisdom Bolsters AnalyticsOps Portfolio, Introducing Wiiisdom Ops for Power BI

Business Wire | October 04, 2023

Wiiisdom, the pioneer in AnalyticsOps, today announced Wiiisdom Ops for Power BI, a new governance offering designed to deliver trusted data and analytics at scale. This SaaS-based solution, part of the AnalyticsOps portfolio from Wiiisdom, unlocks and automates new testing capabilities and integrated BI content management workflows for Microsoft Power BI. Data and analytics governance is a critical enabler of business success, yet many people are still spending the majority of their time finding and resolving errors. Ventana Research predicts that through 2025, governance issues will remain a significant concern for more than one-half of organizations, limiting the deployment and therefore the realized value of analytics investments. Our research shows two-thirds of organizations consider it very important to improve their data governance and only half of are governing their analytic objects, said David Menninger, SVP & Research Director, Ventana Research. Analytical operations solves this challenge by automating analytics governance, allowing all stakeholders within an organization to mitigate risks and make data-driven decisions. The AnalyticsOps portfolio of products from Wiiisdom, including the new solution, Wiiisdom Ops for Power BI, helps to ensure data is accurate, up-to-date, and consistent for trusted analyses. Wiiisdom is on a mission to simplify and automate governance for analytics so decision-makers have quality, trusted data that they can use for decision-making. By streamlining testing, deploying, and monitoring as an integrated workflow across the entire organization, data leaders can provide timely insights that will drive value to their business, without sacrificing quality and trust. “With more than 15 years of experience solving the toughest business intelligence challenges, we have a unique understanding of the problems that today’s data leaders face,” said Sebastien Goiffon, Wiiisdom Founder and CEO. “The launch of Wiiisdom Ops for Power BI is another step in our mission to minimize risk and increase trust in an organization’s data so business leaders across the globe can confidently make data-driven decisions.” Wiiisdom Ops for Power BI automates dataset and report testing and streamlines analytics governance workflows, so organizations can easily validate datasets and trust that reports are accurate. The new offering allows users to: Test and deploy content at scale: improve productivity and increase confidence with cloud-native, automated testing for all Power BI content across an organization; Catch errors to minimize risk: build and run statistical validations, value checks, regression tests, and more to identify errors, and then document test results to comply with business and regulatory requirements; and Build trust in data and analytics: ensure content is accurate and reliable so organizations can maximize the value of Microsoft Power BI investment and drive stakeholder adoption across the enterprise. The introduction of Wiiisdom Ops for Power BI builds on the tremendous traction that Wiiisdom has achieved over the last 12 months. The company continues to attract top talent, adding several new go-to-market leaders from Tableau, including Michael Holcomb, VP, Customer Success and Jeremy Blaney, VP, Product Marketing, who deeply understand modern BI and customer pain points. Additionally, the company introduced a new partner program that brings together data and analytics consulting partners and world-class resellers to further scale the business. Wiiisdom Ops for Power BI, which was unveiled at the Microsoft Power Platform Conference in Las Vegas, is available now directly and on Microsoft AppSource and Microsoft Azure Marketplace. To learn more about Wiiisdom Ops for Power BI, please check out the blog post or visit: https://wiiisdom.com/wiiisdom-ops/power-bi/ About Wiiisdom Wiiisdom is the pioneer in AnalyticsOps, building no-code, enterprise-grade solutions that simplify governance for analytics and business intelligence platforms like Tableau, Microsoft Power BI, and SAP BusinessObjects. The company offers automated BI testing capabilities and integrated workflows for content lifecycle management that make it easy to comply with governance policies at scale. For more than 15 years, Wiiisdom's solutions have helped organizations of every type, including Fortune 100 companies, build trust in data and analytics, ensuring leaders can make confident data-driven decisions. Learn more at Wiiisdom.com.

Read More

Business Intelligence

Alation Launches Analytics Cloud1 Elevating Data Culture Assessment

Alation | October 12, 2023

Alation, Inc., a prominent data intelligence company, has unveiled its latest offering, Alation Analytics Cloud1. This unified reporting platform empowers organizations to gain insights into their data usage and, in doing so, assess the effectiveness of their data initiatives and the overall maturity of their data culture. The stakes are high in today's data-driven landscape, given the vast opportunities associated with data, analytics, and AI, and organizations can no longer afford to operate with disjointed data efforts that lack a clear connection to value. Alarmingly, a vast majority of Chief Data Officers (CDOs) fail to accurately assess and price the business outcomes generated by their data and analytics efforts, as revealed by Harvard Business Review. Consequently, there is a pressing need for a framework that enables organizations to benchmark and enhance their data management capabilities as they become critical strategic endeavors. However, this void has persisted because organizations have lacked the tools required to quantify the value and impact of their data initiatives on their business operations. Moreover, disparate teams within organizations often employ distinct data analysis methods, emphasizing the necessity for a solution that facilitates the consistent assessment of critical usage statistics, thereby fostering the growth of a robust data culture. The Alation Analytics Cloud offers a framework to articulate the business value of data initiatives. Leveraging Alation's innovative Query Log Ingestion technology, leaders can gain insights into which data sources are most frequently accessed and which teams are executing specific queries. This knowledge enables data leaders to comprehensively map data consumption across the entire organization. These insights serve a dual purpose, facilitating the measurement of the effectiveness of diverse data programs and, subsequently, enabling an assessment of the maturity level of an organization's data culture. These insights can also be harnessed to optimize queries and rationalize data sources, for example, by expediting the migration of frequently used datasets or the retirement of data sources that are no longer in active use, thereby reducing costs. Key benefits of the Alation Analytics Cloud include the ability to: Measure Data Culture Maturity: Organizations can now measure their data culture maturity by examining four vital components: data leadership, data search and discovery, data literacy, and data governance. Score Data Programs: Data leaders are empowered to gauge the progress of their data initiatives using a variety of metrics, such as total assets curated, the number of active users, and many other relevant indicators. Map Data Consumption: Business and data leaders can gain visibility into the efficacy of individual data products by closely tracking actual usage. Reports provide valuable insights into which queries are being executed by specific users on various data stores, including details about the total execution time of database queries, thus pinpointing areas that can be optimized. About Alation Alation is a leading enterprise data intelligence solutions provider, offering capabilities that empower self-service analytics, drive cloud transformation, and enhance data governance. Over 500 leading enterprises, including prominent names like Cisco, Nasdaq, Pfizer, Salesforce, and Virgin Australia, rely on Alation to cultivate a data culture and bolster data-driven decision-making. The company's commitment to excellence is evidenced by its recognition on Inc. Magazine's Best Workplaces list four times, its status as a UK's Best Workplaces in tech and Best Workplaces for Women in 2022, and its continued accolades as a UK's Best Workplaces in 2022 and 2023.

Read More

Data Science

Snowflake Accelerates How Users Build Next Generation Apps and Machine Learning Models in the Data Cloud

Business Wire | November 03, 2023

Snowflake (NYSE: SNOW), the Data Cloud company, today announced at its Snowday 2023 event new advancements that make it easier for developers to build machine learning (ML) models and full-stack apps in the Data Cloud. Snowflake is enhancing its Python capabilities through Snowpark to boost productivity, increase collaboration, and ultimately speed up end-to-end AI and ML workflows. In addition, with support for containerized workloads and expanded DevOps capabilities, developers can now accelerate development and run apps — all within Snowflake's secure and fully managed infrastructure. “The rise of generative AI has made organizations’ most valuable asset, their data, even more indispensable. Snowflake is making it easier for developers to put that data to work so they can build powerful end-to-end machine learning models and full-stack apps natively in the Data Cloud,” said Prasanna Krishnan, Senior Director of Product Management, Snowflake. “With Snowflake Marketplace as the first cross-cloud marketplace for data and apps in the industry, customers can quickly and securely productionize what they’ve built to global end users, unlocking increased monetization, discoverability, and usage.” Developers Gain Robust and Familiar Functionality for End-to-End Machine Learning Snowflake is continuing to invest in Snowpark as its secure deployment and processing of non-SQL code, with over 35% of Snowflake customers using Snowpark on a weekly basis (as of September 2023). Developers increasingly look to Snowpark for complex ML model development and deployment, and Snowflake is introducing expanded functionality that makes Snowpark even more accessible and powerful for all Python developers. New advancements include: Snowflake Notebooks (private preview): Snowflake Notebooks are a new development interface that offers an interactive, cell-based programming environment for Python and SQL users to explore, process, and experiment with data in Snowpark. Snowflake’s built-in notebooks allow developers to write and execute code, train and deploy models using Snowpark ML, visualize results with Streamlit chart elements, and much more — all within Snowflake’s unified, secure platform. Snowpark ML Modeling API (general availability soon): Snowflake’s Snowpark ML Modeling API empowers developers and data scientists to scale out feature engineering and simplify model training for faster and more intuitive model development in Snowflake. Users can implement popular AI and ML frameworks natively on data in Snowflake, without having to create stored procedures. Snowpark ML Operations Enhancements: The Snowpark Model Registry (public preview soon) now builds on a native Snowflake model entity and enables the scalable, secure deployment and management of models in Snowflake, including expanded support for deep learning models and open source large language models (LLMs) from Hugging Face. Snowflake is also providing developers with an integrated Snowflake Feature Store (private preview) that creates, stores, manages, and serves ML features for model training and inference. Endeavor, the global sports and entertainment company that includes the WME Agency, IMG & On Location, UFC, and more, relies on Snowflake’s Snowpark for Python capabilities to build and deploy ML models that create highly personalized experiences and apps for fan engagement. Snowpark serves as the driving force behind our end-to-end machine learning development, powering how we centralize and process data across our various entities, and then securely build and train models using that data to create hyper-personalized fan experiences at scale, said Saad Zaheer, VP of Data Science and Engineering, Endeavor. With Snowflake as our central data foundation bringing all of this development directly to our enterprise data, we can unlock even more ways to predict and forecast customer behavior to fuel our targeted sales and marketing engines. Snowflake Advances Developer Capabilities Across the App Lifecycle The Snowflake Native App Framework (general availability soon on AWS, public preview soon on Azure) now provides every organization with the necessary building blocks for app development, including distribution, operation, and monetization within Snowflake’s platform. Leading organizations are monetizing their Snowflake Native Apps through Snowflake Marketplace, with app listings more than doubling since Snowflake Summit 2023. This number is only growing as Snowflake continues to advance its developer capabilities across the app lifecycle so more organizations can unlock business impact. For example, Cybersyn, a data-service provider, is developing Snowflake Native Apps exclusively for Snowflake Marketplace, with more than 40 customers running over 5,000 queries with its Financial & Economic Essentials Native App since June 2022. In addition, LiveRamp, a data collaboration platform, has seen the number of customers deploying its Identity Resolution and Transcoding Snowflake Native App through Snowflake Marketplace increase by more than 80% since June 2022. Lastly, SNP has been able to provide its customers with a 10x cost reduction in Snowflake data processing associated with SAP data ingestion, empowering them to drastically reduce data latency while improving SAP data availability in Snowflake through SNP’s Data Streaming for SAP - Snowflake Native App. With Snowpark Container Services (public preview soon in select AWS regions), developers can run any component of their app — from ML training, to LLMs, to an API, and more — without needing to move data or manage complex container-based infrastructure. Snowflake Automates DevOps for Apps, Data Pipelines, and Other Development Snowflake is giving developers new ways to automate key DevOps and observability capabilities across testing, deploying, monitoring, and operating their apps and data pipelines — so they can take them from idea to production faster. With Snowflake’s new Database Change Management (private preview soon) features, developers can code declaratively and easily templatize their work to manage Snowflake objects across multiple environments. The Database Change Management features serve as a single source of truth for object creation across various environments, using the common “configuration as code” pattern in DevOps to automatically provision and update Snowflake objects. Snowflake also unveiled a new Powered by Snowflake Funding Program, innovations that enable all users to securely tap into the power of generative AI with their enterprise data, enhancements to further eliminate data silos and strengthen Snowflake’s leading compliance and governance capabilities through Snowflake Horizon, and more at Snowday 2023.

Read More

Business Intelligence

Wiiisdom Bolsters AnalyticsOps Portfolio, Introducing Wiiisdom Ops for Power BI

Business Wire | October 04, 2023

Wiiisdom, the pioneer in AnalyticsOps, today announced Wiiisdom Ops for Power BI, a new governance offering designed to deliver trusted data and analytics at scale. This SaaS-based solution, part of the AnalyticsOps portfolio from Wiiisdom, unlocks and automates new testing capabilities and integrated BI content management workflows for Microsoft Power BI. Data and analytics governance is a critical enabler of business success, yet many people are still spending the majority of their time finding and resolving errors. Ventana Research predicts that through 2025, governance issues will remain a significant concern for more than one-half of organizations, limiting the deployment and therefore the realized value of analytics investments. Our research shows two-thirds of organizations consider it very important to improve their data governance and only half of are governing their analytic objects, said David Menninger, SVP & Research Director, Ventana Research. Analytical operations solves this challenge by automating analytics governance, allowing all stakeholders within an organization to mitigate risks and make data-driven decisions. The AnalyticsOps portfolio of products from Wiiisdom, including the new solution, Wiiisdom Ops for Power BI, helps to ensure data is accurate, up-to-date, and consistent for trusted analyses. Wiiisdom is on a mission to simplify and automate governance for analytics so decision-makers have quality, trusted data that they can use for decision-making. By streamlining testing, deploying, and monitoring as an integrated workflow across the entire organization, data leaders can provide timely insights that will drive value to their business, without sacrificing quality and trust. “With more than 15 years of experience solving the toughest business intelligence challenges, we have a unique understanding of the problems that today’s data leaders face,” said Sebastien Goiffon, Wiiisdom Founder and CEO. “The launch of Wiiisdom Ops for Power BI is another step in our mission to minimize risk and increase trust in an organization’s data so business leaders across the globe can confidently make data-driven decisions.” Wiiisdom Ops for Power BI automates dataset and report testing and streamlines analytics governance workflows, so organizations can easily validate datasets and trust that reports are accurate. The new offering allows users to: Test and deploy content at scale: improve productivity and increase confidence with cloud-native, automated testing for all Power BI content across an organization; Catch errors to minimize risk: build and run statistical validations, value checks, regression tests, and more to identify errors, and then document test results to comply with business and regulatory requirements; and Build trust in data and analytics: ensure content is accurate and reliable so organizations can maximize the value of Microsoft Power BI investment and drive stakeholder adoption across the enterprise. The introduction of Wiiisdom Ops for Power BI builds on the tremendous traction that Wiiisdom has achieved over the last 12 months. The company continues to attract top talent, adding several new go-to-market leaders from Tableau, including Michael Holcomb, VP, Customer Success and Jeremy Blaney, VP, Product Marketing, who deeply understand modern BI and customer pain points. Additionally, the company introduced a new partner program that brings together data and analytics consulting partners and world-class resellers to further scale the business. Wiiisdom Ops for Power BI, which was unveiled at the Microsoft Power Platform Conference in Las Vegas, is available now directly and on Microsoft AppSource and Microsoft Azure Marketplace. To learn more about Wiiisdom Ops for Power BI, please check out the blog post or visit: https://wiiisdom.com/wiiisdom-ops/power-bi/ About Wiiisdom Wiiisdom is the pioneer in AnalyticsOps, building no-code, enterprise-grade solutions that simplify governance for analytics and business intelligence platforms like Tableau, Microsoft Power BI, and SAP BusinessObjects. The company offers automated BI testing capabilities and integrated workflows for content lifecycle management that make it easy to comply with governance policies at scale. For more than 15 years, Wiiisdom's solutions have helped organizations of every type, including Fortune 100 companies, build trust in data and analytics, ensuring leaders can make confident data-driven decisions. Learn more at Wiiisdom.com.

Read More

Business Intelligence

Alation Launches Analytics Cloud1 Elevating Data Culture Assessment

Alation | October 12, 2023

Alation, Inc., a prominent data intelligence company, has unveiled its latest offering, Alation Analytics Cloud1. This unified reporting platform empowers organizations to gain insights into their data usage and, in doing so, assess the effectiveness of their data initiatives and the overall maturity of their data culture. The stakes are high in today's data-driven landscape, given the vast opportunities associated with data, analytics, and AI, and organizations can no longer afford to operate with disjointed data efforts that lack a clear connection to value. Alarmingly, a vast majority of Chief Data Officers (CDOs) fail to accurately assess and price the business outcomes generated by their data and analytics efforts, as revealed by Harvard Business Review. Consequently, there is a pressing need for a framework that enables organizations to benchmark and enhance their data management capabilities as they become critical strategic endeavors. However, this void has persisted because organizations have lacked the tools required to quantify the value and impact of their data initiatives on their business operations. Moreover, disparate teams within organizations often employ distinct data analysis methods, emphasizing the necessity for a solution that facilitates the consistent assessment of critical usage statistics, thereby fostering the growth of a robust data culture. The Alation Analytics Cloud offers a framework to articulate the business value of data initiatives. Leveraging Alation's innovative Query Log Ingestion technology, leaders can gain insights into which data sources are most frequently accessed and which teams are executing specific queries. This knowledge enables data leaders to comprehensively map data consumption across the entire organization. These insights serve a dual purpose, facilitating the measurement of the effectiveness of diverse data programs and, subsequently, enabling an assessment of the maturity level of an organization's data culture. These insights can also be harnessed to optimize queries and rationalize data sources, for example, by expediting the migration of frequently used datasets or the retirement of data sources that are no longer in active use, thereby reducing costs. Key benefits of the Alation Analytics Cloud include the ability to: Measure Data Culture Maturity: Organizations can now measure their data culture maturity by examining four vital components: data leadership, data search and discovery, data literacy, and data governance. Score Data Programs: Data leaders are empowered to gauge the progress of their data initiatives using a variety of metrics, such as total assets curated, the number of active users, and many other relevant indicators. Map Data Consumption: Business and data leaders can gain visibility into the efficacy of individual data products by closely tracking actual usage. Reports provide valuable insights into which queries are being executed by specific users on various data stores, including details about the total execution time of database queries, thus pinpointing areas that can be optimized. About Alation Alation is a leading enterprise data intelligence solutions provider, offering capabilities that empower self-service analytics, drive cloud transformation, and enhance data governance. Over 500 leading enterprises, including prominent names like Cisco, Nasdaq, Pfizer, Salesforce, and Virgin Australia, rely on Alation to cultivate a data culture and bolster data-driven decision-making. The company's commitment to excellence is evidenced by its recognition on Inc. Magazine's Best Workplaces list four times, its status as a UK's Best Workplaces in tech and Best Workplaces for Women in 2022, and its continued accolades as a UK's Best Workplaces in 2022 and 2023.

Read More

Events