Money-Making Analytics (Part 3): Choosing an Action-Oriented Decision-Making Framework
How can you ensure that your analytics will drive users to make decisions? Now that you’ve finalized your strategy and selected the right technology, your third step will help you create analytics that enable taking action.
- By Shikha Verma
- February 10, 2016
Analytics and reports have been used historically to ponder and analyze. Money-making analytics have quite a different purpose. Only create and analyze analytics that encourage decisions and promote action. When you look at analytics, they should tell you what to do next. This feature of the money-making analytics is most critical and different from other types of information only analytics.
According to research by Andrew McAfee and Erik Brynjolfsson of MIT, companies that inject big data and analytics into their operations show productivity rates and profitability that are 5 to 6 percent higher than those of their peers. Operations functions gain a lot from action-oriented analytics as they save time and effort for folks on the floor-- that is, the nuts and bolts crew from the previous article in this series. The easier the day-to-day operations, the more time people have to spend on real thought and analysis. This is not easy to do because the right kind of analytics should be layered with the right kind of enabling framework.
Action-oriented, money-making analytics have certain key aspects in terms of their design and enablement framework:
1. Timely and Integrated
Timeliness is one of the most critical features of money-making analytics. Analytics has to be timely in two regards. First, analytics must address the problem being solved now; it must focus on the biggest drivers now, not the past. Second, analytics must provide the right information to the right people at the right point in the workflow so the enterprise can take the right action. In other words, information must be available at the point it is needed to make a decision -- the enterprise must place the analytics tool at the most opportune moments in a process.
Let’s go back to the license renewals example introduced in the first article in this series. If license renewals value leakage has been identified as a big problem for a software company, its money-making analytics should identify and highlight the gaps in renewals in terms of customers, products, and partners. Furthermore, the analytics should be tightly integrated with the actual renewals process to enable decision-making when renewals quotes are generated and sent to the customers.
In most high-tech companies, license renewal is a complex process across multiple departments. Sales reps typically start the renewal process, then work with sales operations to create initial quotes. Sales operations works with internal operations and data/technology teams if they notice discrepancies. It’s a multi-step process to get the data fixed, the quote revised, and then sent back to the sales rep who initiated the request.
This is a very opportune process for analytics optimization. The enterprise needs to simplify the process, workflow, and technology to reduce the steps, the people, and the technologies involved in this process (sending the right quote to a customer). Let the analytics tool be designed to resolve data discrepancies. If multiple options exist for renewal, the analytics should simplify those options (reducing their number, for example) or recommend an option.
Simplicity is another key element of money-making analytics. In fact, it’s my favorite design criteria. Analytics should be easy to use and self-service oriented. Analytics should be intuitive and easily understood by a wide range of users. The analytics shouldn’t require much training or hand-holding to understand and use. Even though the algorithms underlying the analytics might be complex, the business concepts and logic used should be simple.
If you are considering more than five variables in a predictive-analytics algorithm, the algorithm is likely too complex. A model taking 20 input variables might clearly depict what happened in the past, but the future is best predicted by simpler models.
One of my favorite quotes exemplifies this concept; it’s from Leonardo da Vinci: “Simplicity is the ultimate form of sophistication.”
Strive hard for simplicity and you will find that most business problems have a very simple solution.
3. Decision- and Action-Oriented
Do not spend your time only creating reports or information-only analytics. Each money-making analytics should encourage decisions and actions. Even better, if applicable, embed and integrate the analytics where the action is, as we discussed in the license renewal example above.
To give you another example, if you want to reduce inventory reduction at a manufacturing and distribution company, put the inventory-related analytics and decision-making as close to the supply-chain leaders as possible. Keep in mind that making decisions where they’re needed most doesn’t mean that the analytics shouldn’t compare and contrast areas and bubble-up exceptions for the leaders. Action-oriented analytics is very much exception-based and narrowly focused, but users must be able to see the high-level views and drill down into the exceptions.
Most of the worthwhile incremental business value potential within companies exists at the intersection and integration points of business functions and departments. Most departments do a fair amount of work managing themselves, their targets, and their challenges, but this integration is often overlooked or under-optimized because they are in a no-man’s land; they are not the responsibility of a single department or leaders. In essence, they’re nobody’s responsibility.
Pay particular attention to decisions that are collaborative and require input and participation from multiple departments or groups. These are the most important decisions your enterprise will make and thus must be the optimal focus of your money-making analytics.
For example, sales forecasting is typically the responsibility of the sales team, but forecasting is at the intersection of sales and supply-chain operations. If you witnessed finger pointing during your initial discussions with the sales or supply-chain team, you might have heard: “Sales always over-commits and doesn’t give the supply chain team enough notice” or “The supply chain team never delivers on time and customers are so unhappy.”
This is clearly an area where money-making analytics can help sales, the supply chain team, and your customers. Put a decision-making framework together with leaders from both teams, set a cadence for review and action, and you will see a tremendous, measurable value created in your company. Be sure to monetize the value and measure it regularly.
Last but not least, be sure to add the right incentives for achieving the goals set by the analytics program. These could be monetary incentives for achieving revenue enhancement or cost-reduction goals identified by the money-making analytics. Incentives could also be simply recognition. Whatever works best in your company’s culture.
The last article in our series provides specific steps to drive the collaboration and training on the money-making analytics. We’ll explain why and how to drive the collaboration across multiple units which likely haven't worked together before.