Data management has a perennial challenge: Meet the latest market challenges without distracting or hampering the company’s core operations. Thus far, 2017 has shown several successful new data projects that shore up vulnerabilities and fix the latest shortcomings. Here’s what the experts see as the latest data strategies in today’s fast-moving environment.
Using IT for Alignment
Much like data management, alignment is a frequent challenge for companies with highly separated departments that don’t share the same goals, contacts, or (sometimes) sense of time. But while alignment has caused much gnashing of teeth in its time, leaders are turning to IT for new solutions, and finding that new data strategies are finally stepping up to deal effectively with alignment issues. Sometimes this can be as simple as making sure different departments and teams are all using the same app so that information can be easily exchanged. Sometimes it means implementing more complex collaboration software. Either way, the result is better communication, more timely action, and a better solution to alignment difficulties.
Investing More in Talent Acquisition
The data field has a growing problem this year – a lack of talent among the industries looking for new hires. IT and data specialists are still graduating with the necessary skills, but demand has started to outpace them, and that is leading to a lot more competition for data hires who know what they are doing. It’s no surprise that a popular strategy among experts is investing more in IT talent acquisition. That means a greater focus on benefits, on-the-job training for better certifications, and more effort to meet data specialists where they are, both online and in college. Remember that old saw of business: First get the right team, and then make the right plans.
Pinpointing “Data Lead” Positions
While we’re on the subject of hiring, it’s worth noting that another popular strategy on the rise this year is what some CIOs are calling the data lead. Basically, this position acts as an interpreter for Big Data and data analysis. They take a look at the information that the company is bringing in, wraps those results up with a bottom line, and delivers the results to decision makers higher up. This is a particularly useful new position for companies that are embracing a lot new customer data or logistics information, and aren’t sure what to do with it yet.
Using Data for Greater Automation
Most industries are considering if automation can help their particular challenges. Some of the best results are happening when automation and data synergize. We’re seeing a lot of success here when it comes to IT and customer service – specifically, growing solutions like chatbots, which review datasets and automatically provide requested information to online users, saving time and increasing satisfaction among customers.
Increased Virtualization for Mobile
Mobile devices have become ubiquitous and necessary, but they aren’t perfect. In particular, it’s usually too dangerous to store sensitive business data on mobile devices like smartphones. No matter how strict the guidelines, businesses can’t do anything to prevent the vulnerabilities that hackers continue to discover in these devices. As a result, virtualization strategies are on the rise: The goal is to keep data housed in central locations where it can be more easily secured, while using necessary gateways that allow mobile devices to access that data when necessary, but not store it permanently.
The War Against Complication
This is a common CIO goal this year: Strip away complications with new data strategies that remove older, unnecessary software and legacy systems in favor of simpler solutions and results-oriented apps that make using data far more efficient. If you have run into app compatibility or complexity issues due to different data platforms, consider a “complication audit” to find out where the knots in your system are so that they can be untied.
Migration Toward Vendor Support and Consolidation
In previous years we noted that many companies, especially larger companies with in-house talent, were opting to create their own proprietary data management systems. This approach is swiftly falling out of favor due to the cost of maintenance and general inflexibility of such solutions. In-house creations are being supplanted by more reliance on vendors and consolidation.
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