Business Intelligence for Actionable Insights: Top BI books to Explore

Business Intelligence, Big Data Management, Big Data

Article | July 10, 2023

Every business tries to improve their return on investment (ROI) every year by deploying different marketing strategies and technologies. Businesses are constantly adding new technologies to their content stack in order to enhance their efficiency and boost their revenue and growth. Data is inevitable in today's digital era, and Dan Zarrella correctly describes its role in marketing. He stated, "Marketing without data is like driving with your eyes closed." Indeed, the development of better marketing analytics tools and methodologies in recent years has provided business leaders with tremendous added decision-making power. Marketing analytics enables businesses to harness data points about their prospects and their journey through the selling process to enhance the effectiveness of their go-to-market efforts while optimizing ROI. The benefits can be experienced across teams and business segments. According to Hubspot, over 75% of marketers are reporting on how their campaigns are directly influencing revenue because of marketing analytics tools. So, let’s dive deeper and understand why marketing analytics matters. Why Does Marketing Analytics Matter? Marketing campaigns are just tossed into the world with little or no information about how your target audience responds to your marketing strategies. This happens in cases where business analytics tools are not used. Without employing marketing analytics, it can be said that a business is operating in the dark. Here are the reasons why marketing analytics matters. Quantifiable Actions Marketing analytics tools provide you with reliable matrices and insights into the varied marketing strategies that are implemented. Whenever numbers are presented, concrete data for the marketing effort is provided. For example, if you launched a content marketing campaign and have reliable data, it's easy to see that overall sales improved as a result of that marketing push. Campaign Analyses Only marketing analytics can provide a complete overview of how a marketing campaign or strategy actually performed. The data can be dug deeper to track individual messaging across a broad spectrum of outlets, making sure no approach is wasted. Plan for the Future Once you have an understanding of which marketing strategies are meeting expectations, you will be able to plan strategically for future marketing initiatives. Not only is this helpful for organizing marketing efforts, but it also makes it easier to allocate funds across boards. Maximize ROI with Marketing Analytics When marketers use marketing analytics tools, they can find patterns and signs that can be used to improve the performance of their company. This data can assist account managers to acquire new prospects, reallocate marketing expenditures to the most effective channels, and forecast future possibilities. Integration of marketing analytics software into the sales process can save time, boost revenue, and maximize ROI. Lead Prospecting Marketing analytics can enhance customer acquisition in multiple ways. Many marketers merely acquire data about website visitors and ad viewers via ad networks. They just receive basic demographic data, not tips about how to convert leads to sales. Marketing analytics tracks every prospect in your sales funnel or website in real-time. With a detailed picture of your potential customers, you can recognize qualified leads and target them with marketing. Using data insights, you can boost sales, get rid of bottlenecks, increase conversions, and find opportunities that were hidden in plain sight. Campaign Performance Monitoring Online advertising and marketing have the distinct advantage of allowing campaign managers to keep checks on ad performance in real-time. Businesses can use marketing ROI metrics like clicks, impressions, and conversions to figure out which ads work best. Real-time campaign monitoring is a valuable tool for today's marketers. Placements that are underperforming are paused or modified, while those with a great ROI could get extra ad revenue. These insights usually result in more efficient ad spending. Information from different media channels and data from online applications can be put together to learn about the prospect-to-customer journey. Demand Forecasting With suitable data at the right time, marketers gain more power. Tracking historical data is essential to identifying patterns and predicting demand. Seasonal patterns, for example, can have a significant impact on how well a campaign performs. Detailed research can indicate these factors and assist you in re-allocate or altering your marketing investment. Understanding the product or campaign performance helps to identify which items will be in high demand in the future quarter through the use of marketing analytics. Boost Sales Consumers are more knowledgeable than ever before. Reviews, social networks, blogs, etc., now influence most purchasing decisions. Marketing analytics provides valuable information. Focus on how marketing impacts sales to evaluate ROI. When to contact a potential customer, which product would have the most impact, and who is best suited to close the deal. Find sales-boosting marketing strategies. Marketing analytics can enhance revenue by: Understanding the decision-making process of a consumer. Tracking website user behavior and sales trends. Discussing your ROI strategy with the entire company, rather than just the sales or marketing teams. Summing Up For marketers, the use of marketing analytics technology is undoubtedly going to grow over time. You can boost your marketing ROI by using the best marketing analytics tools. Marketing ROI is mostly determined by how successful you are at developing and executing your company's marketing strategy. If you use the right marketing analytics, you can cut your marketing costs, make more people want your brand, and increase sales. FAQ What are the main components of marketing analytics? An effective marketing analytics strategy must have the following three capabilities: Scalability: Your approach must be able to grow and adapt to the changing requirements of the future. Sustainability: Having the appropriate team is essential to long-term sustainability. Affordability: Analytical is a sound investment, but the budget must be in sync with projected growth. What technology do most marketing analysts use? Marketing analysts can require various technologies related to: Statistical analysis software (e.g., R, SAS, SPSS, or STATA) SQL databases and database querying languages. What is digital marketing analytics? Customer behavior is translated into actionable company data through digital marketing analytics. Businesses can use digital analytics tools to learn more about what customers are doing online, why they're doing it, and how this behavior can be used in digital marketing campaigns.

Business Intelligence, Big Data Management, Big Data

Article | July 18, 2023

Explore the top industry events of 2023 and leverage the potential of big data. Stay updated on the latest advancements and insights while benefiting from the knowledge and expertise of leading professionals across diverse industries. Introduction Data is ubiquitous in the modern digital world, and businesses are struggling to keep up with its rapid growth. To stay competitive, it's essential for businesses to harness the power of big data, and attending industry events is one of the best ways to keep up-to-date with the latest trends, insights, and techniques in data analytics. As we move into 2023, there are numerous conferences and workshops on the horizon that offer opportunities for professionals to enhance their knowledge and skills in this area. This article will highlight some of the most significant events to attend in 2023 for those looking to gain insights and learn how to leverage big data for business success. World Data Summit May 17-19, 2023| Amsterdam, The Netherlands The World Data Summit's three-day conference aims to provide insights on how to leverage data analytics for business transformation. The summit will bring together experts, thought leaders and professionals from various industries to discuss the latest trends, innovations, and challenges in the field of data analytics. In addition, attendees can benefit from practical case studies and market overviews, along with a deep dive into customer analytics or increased technical knowledge, by opting for designated workshop days. Intensive Data and Analytics Workshop IV June 5-8, 2023| Orlando, Florida The Intensive Data and Analytics Workshop IV is a four-day event that aims to equip attendees with comprehensive knowledge and practical experience in data analytics. The workshop covers the latest trends and techniques, as well as ways to utilize data to make strategic business decisions. Participants can engage in a combination of lectures, case studies, and hands-on exercises to fulfill a range of learning objectives. These objectives include identifying tools for integrating data analytics, comparing different data analytics tools, applying various data analytics tools, and exploring the use of data analytics tools in the accounting profession. Data + AI Summit 2023 June 26-29, 2023| San Francisco The Data + AI Summit gathers thousands of professionals in the fields of data, analytics and artificial intelligence. The event is aimed at sharing knowledge, best practices, and ideas, along with featuring presentations from data teams who are revolutionizing their industries. Attendees will learn about building and applying LLM to their businesses and will be offered hands-on training and role-based certifications to upskill their expertise. In addition, they will be able to network with peers, hear from industry experts, and explore the latest technologies and solutions in the field. Data Science Week 2023 June 26-28, 2023| Frankfurt, Germany Data Science Week 2023 is a global forum that aims to showcase the latest developments in the field of data science, including computational methods, statistics, and mathematics. The event will bring together data scientists, analysts, visualizers, statisticians, mathematicians, and researchers working to advance the fundamentals of data science and its applications across various fields, including science, engineering, technology, and society. The inclusive conference focuses on academics and industry professionals, providing a platform for engagement, building connections, development, and learning from data science leaders and experts. 2023 ANA Data, Analytics & Measurement Conference August 21 – 23, 2023 | Chicago, ILL. The ANA Data, Analytics & Measurement Conference, presented by Google, highlights the significance of measurement and the value of implementing a data-driven marketing approach. The conference aims to go beyond numerical data by offering a program that educates and motivates attendees to harness the potential of the available data and utilize it to its fullest extent. This three-day event features comprehensive coverage of critical topic areas, such as the latest developments in privacy regulations, emerging platforms, the changing TV industry, audience insights, and the latest advances in measuring outcomes and impact. In addition, attendees will benefit from real-life examples that they can directly integrate into their business applications. Predictive Analytics World Business June 18-22, 2023| Las Vegas Predictive Analytics World (PAW) is a leading cross-vendor conference series for the commercial deployment of machine learning and predictive analytics. Attendees will learn from experts about advanced topics such as causal inference, churn modeling, cloud-based machine learning, and customer lifecycle analytics. By attending, they can stay up-to-date on the latest trends and techniques, network with peers, and gain insights to improve their businesses. PAW is an excellent platform for learning, exchanging ideas, and making valuable connections. Big Data LDN (London) September 20-21, 2023| Olympia, London Big Data LDN (London) is the UK's leading free-to-attend conference and exhibition for data and analytics. It offers attendees the opportunity to learn from leading data and analytics experts and develop effective data-driven strategies. The event gathers industry experts and thought leaders worldwide to discuss the latest trends and developments in Big Data, including data science, machine learning, artificial intelligence, and analytics. With over 180 technology vendors and consultants in attendance, attendees can discuss their business requirements and network with industry peers. The event features 300 expert speakers across 15 technical and business-led conference theaters, offering real-world use cases and panel debates. Data & Analytics Live July 25-26, 2023| North America Data & Analytics Live is a two-day event that brings together professionals in data and analytics from all over North America for learning, networking, and collaboration. Whether you're a newcomer to the field or an experienced leader, this event offers valuable insights and takeaways to help you and your team throughout the year. Discover how data and analytics are transforming businesses, get answers to your biggest questions, and find inspiration for even greater success in 2023. Attend cross-industry sessions to learn and collaborate with like-minded attendees to solve shared problems during discussions and Q&A with speakers. Connect with attendees and build new collaborations through interactive sessions. Learn about the latest advancements and trends in Data and Analytics from expert speakers and develop your leadership skills through interactive Executive Coaching sessions. DataConnect Conference July 20-21, 2023| Columbus, Ohio The DataConnect Conference is a yearly event that brings together entrepreneurs, technical experts, and industry leaders to explore the latest advancements, trends, and innovations in data, analytics, machine learning, and AI. Attendees can access top-notch content delivered by presenters who are at the forefront of data science, analytics, AI, and machine learning over two full days. DataConnect covers various relevant topics with multiple tracks, ensuring every attendee can create a personalized agenda tailored to their interests and experience level. The event has a global presence, attracting professionals from various industries, roles, backgrounds, and skill levels. Attendees can also enhance their experience by adding expert-led workshops. They can learn in-demand skills from professional-level instructors in 4-hour workshops covering a broad range of topics. Data Architecture Online July 12th, 2023| Online Data Architecture Online is an annual event that covers the latest strategies and technologies for building and managing modern Data Architecture. Attendees can join live, webinar-style sessions and learn from industry-leading professionals while connecting with other data peers. The event is free and designed for data architects, modelers, database administrators, and IT managers of all experience levels. Whether you're new to data architecture or an expert, Data Architecture Online is the perfect opportunity to stay up-to-date on the latest trends and best practices in the field. Conclusion Attending big data events in 2023 presents an outstanding opportunity for professionals and businesses to remain abreast of the latest trends and techniques in the industry. Covering an array of topics such as data architecture, predictive analytics, and artificial intelligence, these events offer practical insights that can help businesses thrive. Whether attending in-person or virtual conferences, there are a variety of options available that cater to the needs of all individuals and organizations. By networking with peers and industry experts, attendees can learn from other's experiences and establish valuable connections. These events provide individuals and organizations with the means to harness the power of big data to drive innovation and success in their respective fields.

Business Intelligence, Big Data Management, Big Data

Article | August 17, 2023

Introduction Machine Learning (ML) has taken strides over the past few years, establishing its place in data analytics. In particular, ML has become a cornerstone in data science, alongside data wrangling, and data visualization, among other facets of the field. Yet, we observe many organizations still hesitant when allocating a budget for it in their data pipelines. The data engineer role seems to attract lots of attention, but few companies leverage the machine learning expert/engineer. Could it be that ML can add value to other enterprises too? Let's find out by clarifying certain concepts. What Machine Learning is So that we are all on the same page, let's look at a down-to-earth definition of ML that you can include in a company meeting, a report, or even within an email to a colleague who isn't in this field. Investopedia defines ML as "the concept that a computer program can learn and adapt to new data without human intervention." In other words, if your machine (be it a computer, a smartphone, or even a smart device) can learn on its own, using some specialized software, then it's under the ML umbrella. It's important to note that ML is also a stand-alone field of research, predating most AI systems, even if the two are linked, as we'll see later on. How Machine Learning is different from Statistics It's also important to note that ML is different from Statistics, even if some people like to view the former as an extension of the latter. However, there is a fundamental difference that most people aren't aware of yet. Namely, ML is data-driven while Statistics is, for the most part, model-driven. This statement means that most Stats-based inferences are made by assuming a particular distribution in the data, or the interactions of different variables, and making predictions based on our mathematical models of these distributions. ML may employ distributions in some niche cases, but for the most part, it looks at data as-is, without making any assumptions about it. Machine Learning’s role in data science work Let’s now get to the crux of the matter and explore how ML can be a significant value-add to a data science pipeline. First of all, ML can potentially offer better predictions than most Stats models in terms of accuracy, F1 score, etc. Also, ML can work alongside existing models to form model ensembles that can tackle the problems more effectively. Additionally, if transparency is important to the project stakeholders, there are ML-based options for offering some insight as to what variables are important in the data at hand, for making predictions based on it. Moreover, ML is more parametrized, meaning that you can tweak an ML model more, adapting it to the data you have and ensuring more robustness (i.e., reliability). Finally, you can learn ML without needing a Math degree or any other formal training. The latter, however, may prove useful, if you wish to delve deeper into the topic and develop your own models. This innovation potential is a significant aspect of ML since it's not as easy to develop new models in Stats (unless you are an experienced Statistics researcher) or even in AI. Besides, there are a bunch of various "heuristics" that are part of the ML group of algorithms, facilitating your data science work, regardless of what predictive model you end up using. Machine Learning and AI Many people conflate ML with AI these days. This confusion is partly because many ML models involve artificial neural networks (ANNs) which are the most modern manifestation of AI. Also, many AI systems are employed in ML tasks, so they are referred to as ML systems since AI can be a bit generic as a term. However, not all ML algorithms are AI-related, nor are all AI algorithms under the ML umbrella. This distinction is of import because certain limitations of AI systems (e.g., the need for lots and lots of data) don't apply to most ML models, while AI systems tend to be more time-consuming and resource-heavy than the average ML one. There are several ML algorithms you can use without breaking the bank and derive value from your data through them. Then, if you find that you need something better, in terms of accuracy, you can explore AI-based ones. Keep in mind, however, that some ML models (e.g., Decision Trees, Random Forests, etc.) offer some transparency, while the vast majority of AI ones are black boxes. Learning more about the topic Naturally, it's hard to do this topic justice in a single article. It is so vast that someone can write a book on it! That's what I've done earlier this year, through the Technics Publications publishing house. You can learn more about this topic via this book, which is titled Julia for Machine Learning(Julia is a modern programming language used in data science, among other fields, and it's popular among various technical professionals). Feel free to check it out and explore how you can use ML in your work. Cheers!

Article | April 20, 2020

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