Data is growing. The digital creation and encapsulation of information moves forward, upward, and outward in an ever-spiraling and unfolding journey of eternal expansion all the time.
Thinking about how we will work with data in the future usually leads IT vendors to make relatively inevitable comments that fall into a broad category of brushes with predictable platitudes. Representatives love to talk about every company becoming a data-driven organization, the need to democratize data literacy across entire departments, and the need to understand why data is every company’s strongest asset—besides its equipment, capabilities of the process and perhaps even its people.
Arguably more interesting are those technology experts who are inclined to talk about how data will now be used on a basic practical level. There is a clear and compelling need to look for parts of the business where data models and datasets can be reused without having to reinvent the wheel every time.
The shift to data value
Another way to do this is to move to understanding the value of data.
According to Satyen Sangani in his role as CEO and co-founder of data management and analytics company Alation, “In 2023, the pendulum will swing from innovation to value. [data-driven decision-making focused] organizations face economic uncertainty’.
Don’t hold your breath, but Sangani suggests we might be hearing less about these tired and confused “innovation” initiatives over and over again. Indeed – beyond innovation, we need to go through Return on Investment (ROI) because we’ve already discussed this too – we now need to know what value a business gets from its data.
It could happen, meaning those IT projects that don’t have a data value line in their spreadsheet simply won’t get funded.
Clearly, this means enabling data democracy and enabling the so-called citizen data scientist, but we should do so carefully and not hand over too much power on two levels a) by putting the wrong data tools in the hands of the wrong people and giving them access to data they shouldn’t be fighting over – and b) trying to take too much responsibility away from professional data scientists (and indeed entrepreneurs) and simply “expecting” relatively average users to find the answers.
Despite these reservations, Sangani still insists that platforms should be built for the non-technical data user. “Breaking down silos by connecting everything and making it easily adoptable and engaging so all users, regardless of role, can find, understand and use data together. Platforms that do not perform this level of cross-organizational data governance and data democratization will become obsolete,” he said.
The rise of data bootcamps
Sangani’s colleague John Wills is field CTO at Alation. Suggesting that 2023 will see a big push for data democracy, Wills uses the term data bootcamps to describe what could (or perhaps should) happen now.
“In addition to a greater focus on data literacy, next year  will be the year of data bootcamps, where organizations are increasingly turning to external data training services for senior employees, helping to both improve the overall quality of data talent while elevating the importance of data and creating an engaging data culture” .
What is data bootcamp? As suggested, this could be a skills initiative day with employees receiving hands-on training in the use of data analysis tools. Aimed at solving constructed problems based on virtual data, users could look for patterns, trends, outliers and flags to guide deeper use of the intelligence tools and ultimately be able to apply them to their own challenges in space their work. After the white guns have been used throughout the bootcamp experience, it would probably be time to let the employees loose on the open battlefield with live ammunition.
There are many more caveats, stipulations, stipulations and limitations for all of this to happen, especially given the reality of the “great layoff” due to Covid and the ongoing reality of home workers. This means that more data is processed remotely, sometimes at the kitchen table.
As previously stated here, according to Alation’s Wills, “This higher turnover rate put additional pressure on already leveraged businesses as they lost talented employees and the critical institutional knowledge that often came with them. For organizations that did not have strong data retention systems in place, lost employee information can be difficult if not impossible to replace. As businesses strive to avoid such losses in the future, more will turn to data intelligence platforms that can store, organize and highlight key insights to mitigate the impact that an employee loss can have on a business.”
More controls, clean kills
We used the terms (above) open battlefield & live ammunition for a reason. as we move down the road to data value (which in this ratio might represent net kills?) and capturing the flag, we’ll need to think about how we balance the amount of funds allocated to complying with data regulations and away from data innovation .
From an industry perspective, automation can help massively here. The Alation team reminds us that so many robust data access and security controls are now automated and have been for some time. But, they say, we can expect data governance automation to combine existing automated operations with data governance policy making to free up time for data teams to focus on business innovation without leaving a business vulnerable to attack.
Again this sounds great on paper, but we all now know how dangerous automatic weapons can be in the wrong hands, right? The mission is data value and everyone carries battle fatigues, some powerful weapons and a range of field supplies.
So now who brought the map and is it security?