7 Common Mistakes Ruining Your Data & Analytics Maturity Strategy — And How to Fix Them
06/10/2019 by Tricia Aanderud Modernization - Analytics
The goal of a successful data and analytics maturity strategy is to help grow the business, improve operational processes, and ultimately become more profitable.
With all the online guides available about maximizing data usage, using machine learning in your business, and dissecting customers into segments, most business leaders are challenged trying to figure out how to make it work together.
If your analytics capabilities are falling short, it’s likely due to one of the following common obstacles:
Most data strategies begin with business intelligence and move toward advanced analytics. If your business has been using data for several years, then your next step may be starting an analytics center of excellence (ACOE) or even incorporating artificial intelligence techniques.
Your analytics strategy is based on which level of the analytics maturity model you are starting. There is not a single approach that works for every organization, but all data and analytics capabilities have some commonalities.
The strategy may address the plan for a single year, or it may span 3 or more years. It ideally has milestones for what the team will accomplish along the way.
The analytic strategy should at least assess the business reasons for what data you’re integrating, what technology is required to be successful, and how to manage any capabilities skills gap that currently exists. Management can understand and support those needs and provide additional resources.
The plan is essential to having everyone in alignment. However, it should not feel like it is set in stone. It can and should change when business circumstances or changes in direction dictate. It is more important for the leadership team understands and believes that the strategy supports the organization.
When you start planning an analytics strategy, the temptation is to solve all problems – or apply the so-called “boil the ocean” approach. With so much data available in an organization, it’s easy to fall into the trap of trying to understand, measure, and improve all processes. For most companies, this is not possible and will not yield the desired results.
Your business may favor specific projects or initiatives to move the company forward over long-term digital transformations. Keeping the project goals precise and directed helps control costs and improve the business incrementally. The analytics strategy needs to answer deeper business questions rather than try to answer everyone that can be thought up.
You should also consider other ways to introduce analytics into the business. Use initiatives that target smaller areas of the company to build competencies. Provide a technology sandbox with access to tools and training to encourage other non-analytic workers to play with the data. Management must support these capabilities. They should willingly provide the needed resources for this digital transformation.
The organization frequently learns that those workers closest to the process are the more informed and thus best resources for providing insights. Many times I have shown baffling charts to a user who quickly commented, “That happens on Tuesdays when Ralph goes to lunch … ” or something similar. Those workers have knowledge that leads you to quicker results.
Even with a focused data and analytics plan, the strategy must be dynamic. Don’t be so rigid that you don’t listen to other ideas or fail to take advantage of opportunities. For this reason alone, many companies have a single-year maturity strategy so they can react to business changes.
Technology implementations take time. That should not stop you from starting in small areas of the company to look for quick wins. Typically, customer-facing processes, such as sales, marketing, or customer service, have areas where it is easier to collect data and show opportunities for optimization and improvement.
There may be some short-term projects that yield big successes and help you move the overall analytics strategy forward. You must mark your data and analytics path with victories to keep your company engaged.
Before any analytics strategy begins, you must understand what questions need answers and why the questions exist. Often the business question may not be presented as an actual question but as a problem. Management might voice concerns about what they fear, what they don’t know, or what they want to improve about their business. Yes, analytics and the right data can assist with finding those answers – but you must be clear about the actual question.
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 needs. Do an assessment of painful issues. Consider collecting intelligence from outside resources or management in other areas of the company.
Consider using actual business cases from your organization to think through the questions. One easy way to generate ideas is to think of expensive issues or even instances where the business was embarrassed in front of the customer. Those situations help others think of where you don’t want to be. When the company can think of pain points, you want to avoid it is easier to get project buy-in.
Many organizations suffer from years of data being mismanaged. A typical scenario is when data sets are merged from multiple sources. As an example, one company acquires another or multiple legacy databases are merged into one. Each source had someone uniquely managing that data. When these sources combined, it can lead to numerous issues if data relationships aren’t understood or if the wrong assumptions about the content are made.
Many projects fail because the time for organizing and cleaning data isn’t factored into the equation. Make sure your team has a clear understanding of what needs to be done at the lowest level for the data to all fit together. Consider implementing a data governance program that encompasses a holistic approach to how you collect, manage, and archive data. Data governance programs sometimes have a negative rap, but they naturally improve maturity.
The more data your organization has available to analyze, the more important it is that data dictionaries and business glossaries are available. These tools prevent users from mistakenly assuming what data columns mean.
Having data users who understand how to use the data increases productivity and promotes useful analytics!
Coaches can be a great resource to companies because of their in-depth analytics knowledge, ability to develop talent and a knack for using data to ensure results. Search for an experienced mentor with a background in driving business transformations with analytics. You want to create an environment where it’s OK to be wrong, to experiment, and to challenge the norm. That is the model you want!
A coach (or advocate) could be someone from within the company or from outside the company. The coach is someone who has domain experience and who understands the business value of analytics. It’s essential this person has the trust of upper management. This individual can comfortably cross from discussions in the board room to working with a small data science team to determine what business goals are attainable and how best to reach them.
If the politics are obtrusive or if there is not a knowledgeable resource internally available, use a third-party coach. This person does not have any loyalties other than ensuring the team can effectively use data to support critical business decisions. This person can easily move across management levels.
An analytics coach helps stubborn leaders who don’t understand how data can expand the enterprise. Perhaps the person asks the leader to predict future business results and then shows how analytics can do that. To demonstrate the value of the data, the coach may use it to confirm what the leader already knows. At that point, the coach can layer in more analysis to show what else the data may know that the leader does not.
For a strategy to be successful, it must be realistic. This means the organization must be able to implement it. If you are getting a lot of pushback in certain areas, you should explore why this is happening. Maybe your assessment of the business maturity was a little overconfident.
Always listen to organizational feedback. Sometimes strategies fail because not everyone’s feedback is valued. It’s difficult to hear negative feedback when you have put your heart into a project. However, your naysayers may have valuable suggestions about why they are not able to support your plan.
Experienced leaders often seek negative feedback for these very reasons. By allowing others to provide feedback you build the opportunity for them to feel more ownership toward the solution. This is the leadership style you want to model.