This is the fourth follow-up article in my series about trends in data analytics. Article one –Climbing the Analytic Maturity Mountain - discusses how organizations are now thinking about unstructured data as a new source of differentiated value. Article two – The Transition to Predictive Analytics – describes how the world is moving from retrospective analytics to predictive analytics as modeling techniques and supporting data have improved. Article three – Deep Data Science vs. End User Analytics – outlines the tradeoffs of choosing to invest in data scientists or in new tools for business analysts.
In this post, I want to focus on the importance of integrating analytic insights directly into business processes to improve decision making and increase agility. This is about bringing the right information to the table just as it’s needed. In some ways, you can think of this as “just-in-time insights,” a business process equivalent to just-in-time manufacturing. We may think of Google as one of the first overtly data-driven organizations, but nearly all of today’s successful companies are beginning to lean on data and analytics to help them make better decisions.
Imagine that you are a sales manager at this company and you are planning to pitch a large pharmaceuticals company. Your expertise is not in pharmaceuticals, so you need help from across your organization. Do we have industry-specific marketing material for Pharma? Have we sold to other companies like this one? If yes, who was involved in those deals, and are any of the solutions engineers from those deals available to join the meeting?
Answering these questions should be simple, but it’s often far from it. This data resides in multiple locations, if it is being tracked at all – and it often isn’t organized, linked, or searchable. Manually browsing for each piece of information takes significant time and not having access to the right information or expertise can lose deals. Imagine being able to quickly search for marketing documentation that contains the term “pharmaceutical,” and view the creators, editors and readers. You could then quickly see who in product marketing is creating this content, and who in solutions engineering has been using it.
Content exploration and expert identification are important first steps. Over time, you could pull data from your CRM system to identify similar deals. This could help streamline deciding on deal structure and initial pricing, by leveraging experience from across the organization. By providing access to this information in the right way, we can dramatically improve a process the company executes frequently. This improves employee efficiency, but more importantly, it improves consistency, predictability, and likelihood of successful sales engagements, by sharing information and experience exactly when and how it’s needed.
So how do you make this a reality in your organization? There are three essential parts to the approach: identify business problems, start small, build on success. Think of it as a virtuous cycle – small wins, using small data, provide fuel for bigger projects using more diverse data sets. For these small wins, start by identifying a problematic business process with output (e.g. renewal rate) or cycle time (e.g. time spent figuring out what to charge) that can be measured. Then, survey the available data that could be used to improve the process and select the highest value data sources for the initial project. A common mistake is to try integrating data from too many sources at once; as the number of data sources goes up, so does the complexity and cost to implement. Starting small is beneficial for a number of reasons. Initial experiments don’t cost much to try, so they don’t need extensive approvals. They also don’t take as long to implement, which allows you to succeed or fail quickly and learn from the process. Finally, these small projects each focus on the biggest problems, so you can make meaningful impacts in multiple areas of the business.
Businesses who use their data effectively are more likely to be successful than those who don’t. In fact, according to a study by MIT Sloan, top performing businesses use analytics five times more than lower performers. But remember that you’ll only get there by focusing on business problems, not data, and taking baby steps the whole way.
Have you integrated analytic technology into your business processes? What successes have you had and what challenges have you faced?