The data revolution has put companies in an interesting position—swamped with information, it is now their responsibility to ascertain actionable insights as fast as possible to avoid falling behind. It’s a challenge to keep up—while there are plenty of tools for collecting big data, and plenty to analyze it as well, the sheer variety can overwhelm a CIO not prepared for the influx.
Because of this, data agility is king. Anyone can collect large amounts of data, but it takes a skill to translate this data into something that companies can take advantage of. Cloud platforms and databases such as Hadoop can help this effort in many ways, but it still falls to the CIO to track recent innovations and keep current. This in of itself requires a strong knowledge of data best practices; no company can adopt every new innovation, but picking and choosing the right scalable data infrastructure can ease the burden of adapting as time goes on.
A big part of a CIO’s responsibility to agile data is choosing a cloud platform that’s right for their business. Different individuals and stakeholders may have a diverse array of opinions on which is best. For instance, Google Cloud Platform may be preferred by a company’s data scientists for its machine learning capabilities, whereas the integration capability offered by Azure may be a good choice for developers. In cases like these, where a company will have to serve a variety of cloud users, it may be smart to consider a multicloud infrastructure to accommodate for as many needs as possible. This approach, though potentially more costly, can ensure that companies are able to harness the developments of each of these platforms in the future.
Beyond infrastructure, other tools exist to help a company maximize its data agility. Apache Drill is one such tool that circumvents the need for IT assistance to query data. It’s an SQL query engine that avoids the problems associated with schema creation while being ANSI SQL:2003 compliant. This and other tools like it are the key to gaining data insight as quickly as possible by cutting down on cycle time.
And concerns about data processing have changed over time. Before, hand coding data architecture was more common, and though it may still be serviceable for small, specialized projects, it is the antithesis to data agility in that it is time consuming to develop and always created for a specific platform. A better alternative for the modern business is data integration software, which takes the burden off of the business and supports new innovations and all types of cloud data.
That said, it takes a bit of vetting to choose the right integration software. The ideal software should be scalable, cross-platform, and allow for real-time data processing. It’s called an agile data fabric, and it’s meant to synthesize all types of data a company will need to work with. Platform agnosticism is important for the same reasons why a multicloud infrastructure is valuable; it allows the company to take advantage of new innovations and specific capabilities.
Organizations should also strive to be self-sufficient with their data. A controlled move to properly distributed data can greatly enhance insight. However, this requires a number of different participants within a company, including IT staff and dedicated data analysts, all with their own needs. A savvy CIO can craft an infrastructure that meets everyone’s needs and allows for scaling as innovation continues its mad rush forward.