Now recently Gartner launched its top technology trends for 2K19. The common thread that holds every technology and trends are Artificial Intelligence, Augmented Analytics, Blockchain and many more and all these have the ability to turn data into a business asset. That future growth lies in the ability to turn data into something useful isn't a surprising revelation. But not every organization is equipped to make the most of its valuable information.
Business leaders who wish to move their companies in 2019 with swift efficiency should look to the following three mechanisms for turning data into value.
Enable Search-Based Business Intelligence (BI)
Gartner defines search-based discovery tools as those that "enable users to develop and refine views and analyses of structured and unstructured data using search terms." As organizations become increasingly preoccupied with leveraging data for gains in operational efficiencies, the ability to easily access that information becomes paramount. The idea of using a Google-like interface for BI has been around for a while. Yet despite the ease with which we open a browser and search for things we want on a daily basis, that same, simple interface for accessing datasets isn't always available in the enterprise.
The original search engines launched in the 90s (hello, Lycos) were the first public displays of big data in action. Over the years, the high-tech industry has sought to build a similar environment, where corporate users realize, economically, scale and processing power for big data analytics. As many of us now know, most of the tools required to replicate that type of big data environment are available today. It's interesting, then, that with so much emphasis on big data analytics, that using a Google-like interface in the enterprise is still restricted to small segments of the available data sets.
The pursuit of making big data analytics easier for all users must be a cornerstone of organizations' 2019 philosophies. And it likely starts with identifying the right underlying technologies. Just as public search engines had migrated their architectures from using "big iron" to using distributed computing on commodity hardware, organizations must identify the right architectures for getting value from search-based BI across all their data. That might ultimately help them to figure out how to approach analysis the same way we approach figuring out where to go to lunch.
IoT the Streaming Data
The internet of things (IoT) emerged as a buzzword not long ago, but we seem to be past its apex and into its post-hype cycle. The thing is, it's still a hot topic, and there are still likely to be over 30 billion IoT devices by the end of this decade. The data they produce must go somewhere. It's not even just the fabled IoT-device deluge that is causing proverbial data streams to overflow. Things like website clickstreams and financial market data also take the form of rivers through an analytics infrastructure. The trick is to drill into the most important parts and turn them into something valuable.
There are two main objectives that organizations must achieve to get the most out of streaming IoT data. The first is defining how to deliver insights immediately in order to take immediate action and leverage a recent event. While automation typically lends itself well to taking immediate action in real time, there are many scenarios that require human input. One example operations optimization might require a system to detect events indicating an inefficiency or error. The second objective is to provide a user interface that a broad, typically non-technical audience can use. IoT analytics is often seen as a technical domain with data scientists and data engineers leading the way, but with a limited talent pool, organizations must enable, with the right tools, business users to find insights in streaming data.
We hear a lot about athletes using the tactic of positive visualization. "Visualize your swing before your next at-bat." It's a way for those in their peak physical power to link mind with body.
With the tools at the disposal of organizations today, it's time that business leaders took a similar tack with their approach to 2019's data strategy. Artificial intelligence (AI) has emerged as a high-priority technological pursuit today, but there's a risk that AI is only used in the domain of highly technical personnel. With a strategy around visual analytics on AI, you can have more types of users understand outputs from AI-driven analysis, such as capturing customer sentiment, streamlining business operations and predicting upcoming problems.
The key objective with enabling visual analytics on AI is to open that capability to everyone, including the non-technical business users. Chances are, your organization doesn't have time to wait for a data scientist to respond to requests for analytical outputs. You need the insights from your data now, and you need the people directly responsible for acting on those insights, regardless of job role, to be able to open a dashboard and understand what's happening based on the work of your data scientists. Chances are, you're unlikely to get far if the best means of communicating analysis is through a complicated spreadsheet. A sheet filled with numbers isn't for everyone but user-friendly charts, graphs, and dashboards are. Hence, data is the key to a competitive edge, and it's time to turn that information into insight, today.