How to Overcome Challenges in Adopting Data Analytics

AJINKYA | April 20, 2020

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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.

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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.
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What are the Benefits of Data Modeling for Businesses?

Article | January 21, 2022

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7 Apps to Learn and Practice Data Science Effectively

Article | January 19, 2022

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Article | January 12, 2022

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Effective Ways to Prevent Data Breaches

Article | November 26, 2021

Data breach prevention is going to be the need of the hour as cybercrime continues to grow. Cybercrime is a growing threat to businesses of all sizes. Due to this unprecedented time many companies shifted to work-from-home model. Statics show data breaches are on a rise and can have devastating long-term financial set-back or reputational repercussions to your organization. As a result, businesses must ensure that their data is secure to avoid substantial loss or theft. As data breaches happens in different ways, there is no such thing as a one-size-fits-all remedy. Security needs a multifaceted approach to be effective. In this article we’ll find out different ways prevent data breaches. Impact of a data breach on businesses A data breach can destroy a business, especially for small and medium-sized businesses (SMB). Data is a valuable asset for any business especially, the data related to customers and payments. Cybercriminals find this data valuable. Lack of planning and security creates vulnerabilities for criminals to exploit. It is estimated that 60% of small and medium-sized enterprises will close within six months after the attack. Larger businesses or agencies, on the other hand, will survive. Nevertheless, they too will suffer the consequences. A data breach can impact businesses in the following ways; Financial Businesses must compensate for both immediate and hidden fines (fines, public relations, legal fees, and punitive regulatory measures) for a data breach. In addition, business needs to compensate customers, refund any stolen funds, and bear a share value loss. A smart organization will use this opportunity to develop data security and disaster recovery strategies, which entails financial investment. Fines and fees – The PCI Security Standards Council may impose fines or penalties for a data breach. Both regulatory organisations and card network brands will have different fines. Forensic investigations – Major consequences of a data breach include, the business that was attacked will be accountable to perform a forensic investigation to determine the causes of the data breach. These investigations are costly and often yield valuable evidence and insights to prevent future data breaches. Future security costs – Victims of a data breach may have to bear costs of credit monitoring for customers whose data was compromised. This may also include the costs of identity theft repair, card replacement, and additional compliance requirements from the PCI. Reputation Having a good reputation is the most prized asset for any organization. As a business, one must constantly put effort into building and maintaining brand integrity. A single compromising episode like a data breach can trash the best of reputations. According to a PwC report, 85% of customers won't shop at a business if they have concerns about their security policies. Customers value their privacy, and a data breach will be perceived as a lack of regard for their data and privacy. Furthermore, 46% of businesses reported that security breaches harmed their reputation and brand value. Intellectual Property The product blueprints, business strategies, and engineered solutions are some of your most valuable assets for any organization. Your trade secret gives you an added advantage over your competitors. Hence it needs to be protected as some may not hesitate to use breached intellectual property. Other significant consequences of a data breach include; A data breach can pit the CEO against the CISO Poisoned search results on your corporate brand Loss of sales after a data breach Unexpected expenses Less attractive to new employees, especially in tech positions Legal penalties after a data breach Understanding the aftermath of a data breach is an important step to safeguarding your business. The next step is to create an action plan is to protect what you've worked so hard on. How does a Data breach happen? Data breaches sometimes can be traced back to planned attacks. But, on the other hand, it can result from a simple oversight by individuals or flaws in the infrastructure. Accidental Insider For instance, an employee uses a co-worker's computer and reads files without proper approval or permission. However, the access is unintentional/accidental, and no personal information is revealed. The data was breached, however, because it was read by an unauthorised person. Malicious Insider This person deliberately accesses/shares data with the intent of causing harm to an individual or company. The malicious insider may have genuine authorization to use the data, but the intent is to use the info in nefarious ways. Lost or Stolen Devices Any laptop or external hard drive with important information on it that is not encrypted or unlocked goes missing. Malicious Outside Criminals These are hackers who attack several vectors to collect information from a network or an individual. Global cost of data breach According to the Ponemon Institute's Cost of a Data Breach Report, global data breaches cost $3.86 million on average in 2020. The amount in 2020 was somewhat lesser compared to 2019 when it hit $3.92M. The same report found that the average cost of a data breach in 2020 totaled $8.64M. Ways to prevent a data breach Conduct employee security awareness training Control access to data sensibly Update software regularly. Require secure passwords and authentication Simulate phishing attacks Evaluate accounts Limit access to your most valuable data. Review your user account lifecycle processes Insist on complex and unique passwords Protect against authentication bypass Store sensitive personal information securely and protect it during transmission Consider implementing a secure SSO solution Secure all endpoints Segment your network and monitor who's trying to get in and out Manage Vendors - Third-party vendors must comply. Conclusion Protecting against data breaches may appear to be a time-consuming procedure. You will be in a better position if you take an encrusted step to secure your data using various methods, policies, and procedures to ease security threats. FAQ’s How does a data breach impact an organization? Depending upon the company and data type, the consequences may include destruction or corruption of databases, leaking of confidential information, the theft of intellectual property, and regulatory requirements to inform and possibly compensate those affected. What is the most common data breach? Hacking attacks are the most common cause of a data breach. However, it is often a weak or lost password that is the vulnerability that the opportunist hacker is exploiting. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How does a data breach impact an organization?", "acceptedAnswer": { "@type": "Answer", "text": "Depending upon the company & data type, the consequences may include destruction or corruption of databases, leaking of confidential info, the theft of intellectual property, and regulatory requirements to inform and possibly compensate those affected." } },{ "@type": "Question", "name": "What is the most common data breach?", "acceptedAnswer": { "@type": "Answer", "text": "Hacking attacks are the most common cause of a data breach. However, it is often a weak or lost password that is the vulnerability that the opportunist hacker is exploiting." } }] }

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