The old adage ”garbage in, garbage out” is as relevant in today’s Big Data world as ever. For this reason, many organizations that are looking to move up the business intelligence maturity model are articificially being held back from true business insights.
To begin with, let’s talk about the standard analytics spectrum, as defined by Gartner: descriptive, diagnostics, predictive and prescriptive. Descriptive analystics are, as expected, merely a collection of what is going on in your business environment. The common question, “What happened?” applies here. From there, diagnostic analysis helps answer the more complex question “Why did it happen?” Moving up the maturity model, predictive analystics can answer “What will happen?” and the holy grail of analytics: prescriptive, helps answer “What should we do when this happens?”
Most firms see the final, prescriptive analysis component, and think “This looks amazing! How can we get to this point as a business?” The answer is the same for every organization: it begins with descriptive analysis, which begins with good data. Sadly, many firms with strong systems of record still have many blind spots when it comes to ensuring that data has been properly entered. To use a common example: in many places CRM is a critical system of record for sellers. Yet most internal and external sales teams are compensated based on different metrics: internal teams often on the number of touches (phone calls, emails, etc) and qualified leads, whereas external teams are compensated based on deals closed. Because external teams may not be compensated on the number of touches per customer, the amount of data that exists for many potential sales targets varies greatly as leads become opportunities.
This, in turn, can create challenges when one asks “what happened?” If a particular account has “gone dark” that may mean that an external sellers has stopped talking to them, or it could mean that they continue to talk them each week but that the deal progress is not being accurately captured within CRM. When organizations move to “why did this happen?” question, they may start to draw the wrong conclusions based on the inaccurate data within the system.
When I chat with many executives, the answer is invariably the same, “we just need to ‘work harder’ to ensure the data is accurate”. While glib, there are often structural reasons why these inaccuracies persiste, even in very mature industries with high levels of CRM (or other systems of record) adoption. The biggest contributor to bad data is often that there simply isn’t a business pain point associated with it: unless sellers are handing opportunities off to other individuals (the traditional inside to outside sales transition, is often a creator of pain points) there may be no personal reasons sellers might need to adjust their process. These “negative externalities” can occur at all stages of the process and in order to rectify them, companies often either assign a person to “own” that part of the process (thus creating pain where there was no pain) or to incentivize sellers for properly entering in their information or to prevent sales from moving forward (a gated strategy) until those fields have been properly submitted. It is critical for organizations that use the gated strategy to either have a human reviewing the information or to narrowly scope it to prevent sellers from filling the field with bad data merely to let them close it.
With any painful situation, fortunately, there exists an opportunity. For us and the clients we advise, the lack of quality data on a particular subject helps illuminate the most important part of any business intelligence discussion. More important than the overall maturity of an organization’s approach, or the types of tools they use, is the need to solve a specific business problem and to understand the full scope and impact of that problem. With a painful enough problem, all organizations will muster the resources to change. Knowing that a particular type of data is needed to help the business as a whole may be just the catalyst for an organization to put in place the correct incentives to get the entire team working effectively.
Once the incentive structure has been modified, and good data is flowing into the solution, then the focus may pivot from mitigation to problem-solving. In this sort of environment, with good data readily available, the tools already exist (e.g. the cloud-based PowerBI services from Microsoft) to move rapidly up the maturity model to help you reach presciptive guidance. Want to help your organization make this journey? Reach out to New Signature today!
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