If you’re curious about the analytics trends that are most likely to impact enterprise IT in 2016, Information Management’s take is a smart read. There are six trends in total, ranging from data security to Internet of Things (IoT), and data science—and each of them presents powerful challenges to your enterprise data architecture. The bottom line is that these aren’t challenges you fix with an application or a one-off project. IT managers and architects who are responsible for managing the data involved in these trends need a scalable data management architecture to support these innovations.
Even if you can’t make a single, transformative investment to evolve your data architecture, you can still tackle each challenge in a way that moves you a little closer to that vision. Let’s take a look at each of the trends and how it will challenge your data architecture.
1. Cyber security: Offense can be the best defense. A perimeter defense of the enterprise won’t cut it. Data moves, flows, and is transformed and combined. The challenge is to identify which data is sensitive and to manage it effectively. Your data architecture must enable you to identify sensitive data as it enters the organization and track it as it moves around your organization.
2. Companies struggle to bridge the data talent chasm. Big data analytics lets organizations unlock answers to questions that they never thought of even asking before. But, the people who know how to do this are very rare and expensive. Worse, according to The New York Times, they spend 50-80 percent of their time doing data prep instead of delivering business insights. You need to get more from your top talent. For that, your data architecture and tools must work across all types of data: big data; structured, unstructured, and semi-structured; streaming; cloud, etc. You need one set of tools for all data prep. This means less time on “data wrangling,” and more time on producing deeper and more insightful analytics.
3. Man/machine partnerships are getting stronger. Smart machines and cognitive solutions improve the levels of service to customers, patients, etc. But, can your data architecture provide the data these applications need to be effective? Can you onboard data quickly and turn it into clean, complete, and timely data for these applications? This will often mean supporting real-time or streaming data for quick decisions and recommendations.
4. The Internet of Things, and people, too. Sensor data is going to give leading organizations a real competitive advantage. Whether it’s sports equipment that provides immediate feedback to users, or hospitals that react to patient information in real time, the level of what we call “service” is accelerating dramatically. That’s why your single-enterprise data architecture must be able to handle massive amounts of data, filter for the events that matter, and deliver them to the appropriate applications quickly. And, it all has to be highly automated.
5. Triumph of the scientists. This overlaps with trend #2. Organizations will rely on scientists, particularly data scientists, to provide a competitive advantage. Can you free them up from the drudgery of data prep? Customers tell us that half the battle is even finding and understanding the data they already have. You will need an architecture and tools that can help your data scientists work faster and smarter with your data.
6. The rise of the insight-driven organization: Analytics expands across the enterprise. Analytics isn’t something that just a few Business Intelligence (BI) experts and data scientists are doing off in a corner. You’ve got to have a plan to integrate your analytics into your applications and business processes so that they can provide immediate value to your business users and customers. For example: Recommend next best steps for sales people or customer service agents, or for a marketing campaign. That requires data from many disparate sources, delivered quickly and with great data quality.
Conceiving, sharing, and selling your vision
Information Management is right—these are going to be key trends in 2016 and beyond, particularly for market leaders who hope to be disruptors, rather than the disrupted. You’ll want to optimize for these trends while improving the overall productivity of your organization.
Recently, I’ve blogged about the step-by-step approach to building a sustainable data management architecture and how to build the business case for an investment in data management. You may also want to take a look at our recent SlideShare presentation on “Why next-generation analytics projects fail.” It covers the larger contexts that can undermine your efforts.