Don’t forget the routine tasks that make big data work for your company.
While most companies have focused on training staff and understanding big data technologies, a majority have yet to focus on the more routine tasks surrounding big data—such as, how to ensure that everyone who is supposed to have access to the data does, that corporate investments are appropriately portfolioed so the company has visibility of its entire big data investment, and that business decisions are made on good data.
Most of these tasks are routine. They can be undertaken without having to make expensive investments in new software. However, what performing these tasks does require is a commitment from IT and the company to give staff time away from other projects to make sure that all of the daily big data stewardship tasks get done.
Here are seven on-the-ground big data strategies for 2021 that require an investment in basic elbow grease and that can help organizations leverage their big data.
1. Optimize your CIO and CDO relationship
In some organizations, the CIO assumes CDO duties as well as those duties that relate to the core systems in the company. In other companies, there are two separate roles. The CIO focuses on core and legacy systems, while the CDO focuses on digitalizing data assets that include big data.
Those organizations most successful in blending the responsibilities of the CDO and the CIO are organizations in which the two individuals work closely together to coordinate digital and IT strategies. In cases where the CIO does both, digital and core IT system strategies must also be orchestrated so the company can derive the most benefit from both.
By ensuring that CDO and CIO strategy setting and project execution are closely orchestrated, companies position themselves to get the most out of their big data and their systems.
2. Button down Security
Securing big data and systems is a fundamental stewardship function that must be relentlessly performed.
One key is to ensure that all big data and systems, whether they reside in IT, the cloud, or in end business units, are secured with uniform methodologies and standards. This is an area where IT should assume a central and active role.
IT should also have a program for training (and retraining) end users who have security responsibilities for big data in edge environments.
You want to ensure that all IT staff, whether in the central data center or in a remote field office, are using the same security policies and practices.
3. Achieve big data visibility
Someone (likely IT) needs a window into every cache of big data and systems throughout the enterprise. Without this, systems (and data) can be unknowingly replicated, with dollars will be wasted. When data from these systems isn’t being used to its fullest, there might be others that could use the data, but who aren’t aware it exists.
IT can address this by investing in an asset management system that detects any new data, systems and entry points into corporate networks. In this way, you get visibility of all system and data assets. This makes it easier to find system and data overlaps or data and systems that aren’t being used.
4. Link big data with the business
One of big data’s soft spots is the inability to identify impactive business cases that build revenue, reduce costs, and improve operations.
Many big data digitalization efforts have been just that: Digitalize the data and stop there. Big data has limited leverage value if it doesn’t address key strategic and operational questions or impact bottom-line results.
In 2021, organizations have to do a better job in identifying business cases in which big data and analytics can make a dramatic difference—and then be able to quantify the impact.
5. Perform internal audits
Big data system audits should be performed at least annually to ensure that all big data resources and data repositories throughout the enterprise are known and properly secured and governed. IT does this now with traditional transactional data. It’s time to extend this internal audit practice to big data.
6. Bust silos
Once you identify all of your big data assets, you know where the company’s data silos are. These are big data applications and repositories that are sequestered in individual departments, but not shared throughout the company.
Data silos are risky. Independently, departments can make business decisions based on these silos that conflict with what others in the business are doing.
A better approach is to collect all big data in a single repository, and then extract data and move it to different distributed departmental systems. This way, data stays consistent, and everyone sees and uses the same data.
7. Investigate conflicting information
Any time decision makers can’t arrive at or concur on decisions, it’s incumbent on IT to look at the data. Although disagreements or decision inertia may be philosophical, they can also be due to inconsistent or bad data.