5 data analytics investments worth the hype

For nearly a decade, data analytics and business intelligence (BI) have been top priorities for IT investment, and while many analytics investments have driven real value, it isn’t always easy to discern which innovations will lead to productive results and which will be dead ends.

As we transition into 2020, IT and business leaders still list analytics and BI as top innovation investment priorities, says Jim Hare, research vice president at Gartner. After all, intelligence is at the core of all digital businesses. For these leaders, the trick will be seeing through the hype to make the right investments in the right technologies. To help, Gartner has identified five key trends that Hare says will help IT leaders focus on analytics investments that will have an impact in the years to come.

Augmented analytics

“Organizations have more data than they know what to do with and now they’re trying to make sense of that data and turn it into useful insights to help improve decision-making beyond just the business analyst team, but to those users on the front line of the organization that had typically been starved and underserved in having access to analytical insights,” Hare says.

Enter augmented analytics, which uses machine learning to automate data preparation, insight discovery, data science, and insight sharing for a broad range of business users, operational workers, and citizen data scientists — not just the analytics team. This movement toward augmented analytics is one of five key trends identified by Gartner for 2020 — one that Gartner predicts will reach mainstream adoption in two to five years.

Hare explains that data discovery tools have provided business analysts with self-service capabilities, but business analysts have still had to work with largely manual processes.

“You still had to be able to find the needle in a haystack,” Hare says. “The idea of augmented analytics provides almost like a giant magnet that hovers over the haystack to find the needles for you, find the hidden patterns in the data and surface them more effectively. What we’re seeing is augmented analytics using the power of the machine combined with the human user, and together they’re able to work more effectively and actually get more benefit than either of them working independently of each other.”

Digital culture

Hare believes organizations need to focus on developing their “digital culture” in 2020, saying it may be the most important step any organization can take in a digital transformation journey.

“In the past there was a chasm between the business analysts and the end users that really were trying to use more of that data and the analytical insights,” Hare says. “Really a key trend that’s underlying this is a consumerization of analytics and data. Specifically, how do I get more of the use of that data to the forefront of my organization?”

Doing so requires creating a data-driven culture centered around data literacy, especially among those on the front lines of the business, “so people are able to speak the same language when they’re talking about data,” Hare says.

Here, training is the key investment for CIOs to consider as part of a workforce transformation effort toward establishing a digital culture. To promote data literacy, organizations will need to train its workforce to read, write, and communicate data in context, with an understanding of the data sources and constructs, analytical methods, and techniques applied, as well as the ability to describe the use-case application and resulting value, Gartner says.

Hare also notes the importance of training around digital ethics, as the speed at which innovations such as the internet of things (IoT), 3D printing, cloud, mobile, social, and AI are moving makes it highly probable that these technologies will create a gap between morals and actions, leading to unintended consequences. In 2020, organizations will have to reconcile their principles with the possible consequences of the technologies they employ.

CIOs should also consider “data for good” initiatives, Gartner notes. Here, commercial-sector businesses help NGOs and other public-sector organizations that are trying to be more data-driven but lack the skills and expertise to leverage data effectively. This could take the form of free or reduced-cost technology, data, or skilled workers. Such philanthropic efforts can help attract and retain workers in a tight labor market and signal social responsibility to investors, Hare says.

Relationship analytics

Graph, location, and social analytical techniques are helping organizations understand how people, places, and things are connected. Gartner believes the highest-value applications in this space are focused on discovery. For instance, graph techniques can be used to identify illegal behavior and criminal activity, allowing law enforcement agencies to spot money laundering and other criminal activities. Beyond identifying fraud and other illegal behavior, graph analytics has applications in areas such as route optimization, market basket analysis, CRM optimization, supply chain monitoring, and more.

Location intelligence can take the form of services and solutions that generate, process, and analyze data in an indoor environment, or those that garner insight from outdoor geospatial relationships. Indoor location intelligence has uses in areas such as healthcare (monitoring mobile assets, tracking patients), retail (managing staff based on customer traffic, providing turn-by-turn navigation in-store), manufacturing (tracking parts, monitoring idle tools), and the public sector (locating assets and people in an emergency, access control).

Outdoor location intelligence can help with issues such as demographic analysis, store placement, asset tracking, environmental analysis, and traffic planning.

Social analytics is about helping organizations collect, measure, analyze, and interpret the results of interactions and associations among people, topics, and other content. Gartner says social analytics will help organizations spot trends (in customer satisfaction, for example), behaviors (interest in certain topics or ideas), and early warning signals (sources of customer satisfaction and process breakdowns).

Decision intelligence

Hare believes organizations in 2020 will seek to leverage real-time data to drive better decisions. Here, decision intelligence has emerged as a practical discipline that includes innovations such as continuous intelligence, decision automation, and event stream processing.

“This is really sort of the last mile or the last foot,” Hare says. “It’s about looking at decisions, how they’re being made, and what decisions could be automated. I’m thinking in terms of guided recommendations: information that will be given to a human to help them make a better decision.”

Continuous intelligence integrates real-time analytics with business operations to prescribe actions in response to business events based on blend of current and historical data. To succeed, continuous intelligence leverages augmented analytics, event stream processing, optimization, business rule management, and machine learning. Gartner predicts it will take five to 10 years for continuous intelligence to mature but that it will be transformational when it does.

For CIOs looking to invest on a shorter-term horizon, event stream processing is closer to maturity — two to five years out, according to Gartner — but will also be transformative. Organizations seeking to leverage IoT need to get a handle on this innovation soon. Gartner believes event stream processing will eventually be adopted by multiple departments in every large company, supporting operations through real-time dashboards and alerts and anomaly detection. Additionally, it will help organizations save their people from data overload by presenting only the most pertinent information.

Operationalizing data and scaling data use

Organizations will continue to focus their efforts on operationalizing their data and scaling data use. A big part of that will be contextualizing insights for the various constituents within the organization.

“People on the front lines need the analytics much more contextualized for their particular needs,” Hare says. “Someone that’s in sales or marketing or support needs a different set of analytics. In some cases, it’s just about how the analytics is communicated. They need just the right information at the point in time to help them make a particular decision in their jobs.”

As data becomes more ubiquitous inside the organization, part of operationalizing and scaling will be helping individuals avoid data overload by dealing only with the data they need.

Hare says organizations will need to develop a bimodal approach. Mode one analytics are how you run your business, while mode two analytics is about the ability to experiment, uncover hidden insights, and then incorporate those insights into your production analytics. In the past, even organizations that made use of both modes tended to keep them discrete.

“What we’re going to see going forward is a much more blended approach between the two modes,” Hare says. “And you’ll see this sort of continuous process between more quickly discovering new insights and being able to use that in production within the business.”