Smart executives recognize that data is one of the most valuable assets in an organization. Meaningful data can provide intelligence that will aid in business-critical decisions across the spectrum while alerting leaders to trends that need immediate action.
Business intelligence (BI), the transformation of raw data into meaningful and useful information, has long been considered the holy grail of data analysis. The problem is that critical data is strewn throughout a variety of systems, from ERP and CRM to homegrown systems. No single system has all the data needed to make decisions across the board, whether those decisions involve analyzing marketplace trends, negotiating deals with customers, or optimizing operational performance.
For semiconductor and component manufacturers, the problem is further compounded by their multi-tier relationships with their end customers. Contract manufacturers that position themselves as aggregators of purchasing power often obscure the identity of the end customer. Multiple distributors may further convolute the data. This turns the effort of correlating point-of-sale data and direct orders from the contract manufacturer back to the correct end customer into a holistic guessing game. And even though BI offerings promise to merge data into one data warehouse, the promise is naïve. Data from an ERP system doesn't match up with point-of-sale systems, and unless organizations are investing heavily in creating data linkages on a regular basis, making your reporting meaningful will require extensive work. Additionally, once you upgrade one of your systems, your entire data flow needs to be updated.
A cobbled-together analysis may get you close to the truth, but there is always guesswork involved. The data is coming from multiple systems, some records don't match up, and you likely did not collect all the data.
Furthermore, when companies go through the effort to reconcile and analyze data, it tends to be a one-off effort for a board meeting or a standalone event that looks into the past (reviewing the past quarter, for example). Making up-to-date data readily available to a sales or pricing manager on a daily basis is the nirvana of data analysis. It's rare for companies to achieve this with a standalone BI analytics tool.
Truly useful intelligence will track changes in your data and flag changes that require action. What if your data could alert you to the fact that a distributor is bringing you fewer opportunities or is trending down? If your top-line funnel is what you expect, you might not drill down. That means you will unwittingly overlook that Distributor X is creating opportunities while Distributor Y is dropping quarter over quarter. Usable intelligence will track these changes and lead you to the root cause. You'll learn in a timely manner that Distributor Y is favoring one of your competitors.
The right revenue management infrastructure will provide useful intelligence, because it links and automates the complexities of pricing, contracts, and rebates across regions and channels into a system of integrated applications that manage the end-to-end revenue lifecycle. High-tech organizations can make analytics on these complex revenue processes available to the key stakeholders, from the CXOs down to the sales operations manager, at the right level of data.
Making analytics work
With the right analytics in place, you can track changes and thresholds that interest you. For example, you might care to know when any product line has a 5% drop in any region. As a result of these alerts, you might learn that a competitor changed its price, and that you no longer have a competitive price.
Perhaps you want to keep better tabs on your inventory in the channels. Inventory buildup could represent a down business for a key customer, or a competitor could be taking business from you. It's important to understand the implications of changing inventory values. New inventory will quickly devalue old inventory.
Finally, a sales vice president or operations manager might be interested in knowing more about a team's performance beyond who made a quota. Analytics could provide insight into how many new opportunities an individual created, as opposed to achieving all revenue from a previously signed customer.
Useful intelligence will help you find important thresholds, pinpoint the right data, and propose action. BI can have a place in providing useful intelligence, but it is not a comprehensive solution. High-tech organizations should consider investing in an infrastructure that provides a 360-degree view of data that is correctly linked. Otherwise, they will drown in a sea of meaningless reports. As more and more high-tech organizations recognize data as a critical asset, they will need to implement this infrastructure in order to turn data into information and action.