business analytics trends
Which Business Analytics Trends Can Be Put To Use Today?
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Leo Farias
Posted on: December 18, 2018
BI
Tags: business analytics business intelligence predictive analytics Trends
Tags: business analytics business intelligence predictive analytics Trends

Originally published April 6, 2017, updated Dec. 18, 2018.

The BI technologies which offer the best chance of success today are those that allow companies to take advantage of time-sensitive opportunities while providing more responsive customer service.  

One of the most important parts of developing a digital strategy is knowing when not to jump on a high-tech bandwagon.

Some technologies show potential in small-scale trials but haven’t had enough real-world usage to prove their worth to enterprise. Adopting too early puts companies at risk of losing their investment.

On the other hand, waiting too long leaves them in their competition’s shadow.

There’s a lot at stake in this balancing act. Digital transformation isn’t a luxury anymore. It’s critical for companies who want to stay competitive.

Even chains of three or four locations can fall behind their peers if they aren’t maximizing their data usage.

Of course, building up digital infrastructure costs money. Choosing the right technology is the best way to ensure a smooth return on that investment.

A number of business analytics trends are already picking up speed coming into 2019.

Some are years away from being able to deliver on their promises. Others have reached the stage where a company can reliably use them to gain a competitive edge while side-stepping the risks inherent to early adoption.

The best of this second group are outlined below. These are the trends to adopt for enterprises seeking to improve their data agility.

Predictive analytics

Predictive analytics as a field has existed since the late 1600s, when Lloyd’s of London used it to estimate insurance rates on seagoing vessels.

Until the rise of computers, though, it wasn’t a practical means of steering business.

There were too many variables for a human to consider in time to form more than broad predictions.

Widely available cloud storage and increased processing power changed that.

The field has seen a resurgence as the most efficient way to maximize data usage and feed a data-driven decision making process.

73% of companies consider themselves to be analytically driven, and predictive analytics are behind the most successful of these.

Predictive analytics detect deviations in patterns, generate insights based on evolving activity, and predict future outcomes from gathered data.

The benefits of predictive analytics are clearly demonstrated by the variety of practical applications in use today. One unexpected example is human resources.

Retaining experienced workers is a constant challenge for employers who must cope with turnover rates of nearly 20% (averaged across US industries).

The tech sector suffers from even higher turnover. Replacing lost workers can cost up as much as half their annual salary, not counting lost productivity during the training process.

Using predictive analytics, HR managers can find patterns in their employment data that highlight the reasons good employees leave and suggest the incentives most likely to make them stay: higher salaries, additional training, more appealing benefits packages, or in some cases transfers to more engaging positions.

The data also predicts which employees are most desirable to hire and retain.

There’s still a long way to go before the full potential of predictive analytics is realized. That said, the technology is maturing much faster than experts predicted.

Its current capabilities are more than reliable enough to justify making an investment.

Real time analytics

Real time analytics (also known as streaming analytics) give enterprises an up-to-date visualization of their operations.

It was a growing trend back in 2017, and today it’s living up to that promise.

In the traditional analytics model, information is stored in a data warehouse before analysis is applied.

This causes a gap between collection and results where perishable opportunities are lost.

There’s no rule that says data has to be stored first. It can be analyzed mid-stream to sift out data that will only stay relevant for a short time.

Companies then have the chance to make the most of the opportunity through swift action.

Information gathered by real-time analytics is usually displayed in a dynamic graphic format that doesn’t require a data science degree to understand, too. That makes it easy to act on quickly.

A business that can spot opportunities in time to take action makes much greater use than those left playing catch-up on trends.

The one caveat about streaming analytics is that they work best in data-driven cultures. Be sure to provide both technical training and executive support when launching a real-time analytics tool.

Chatbots and Natural Language Processing

Natural Language Processing (NLP) has grown from an internet novelty to a reasonably robust tool.

While it hasn’t seen as much use in the corporate world as its cousin, Natural Language Generation (NLG), it has developed enough for enterprise use.

The most relevant NLP application right now is employing chatbots to provide 24/7 customer support availability. Customers can interact with a chatbot using normal, everyday language.

The sophistication of a bot varies widely. Some have very basic account support capabilities; others can guide a customer from selecting a product all the way through checkout.

At this stage of maturity users generally know they’re speaking to a chatbot, though NLP has evolved to the point where the bot doesn’t frustrate users by getting stuck or spitting out garbled answers.

Instead, bots provide a clear, straightforward path to resolving common customer issues. The convenience of having uninterrupted access to routine account services tends to negate any annoyance.

Virtual assistants also fall under the heading of NLP. These let users request analytics and services using natural language and receive replies either out loud or projected to a specific device.

There are virtual assistant integrations for a huge variety of popular enterprise programs. Some even provide a path for the assistants to complete purchases using pre-approved sites.

Looking forward

Some interesting trends are gaining traction right now. Connected “multi-cloud” strategies are maturing, and research firms like Gartner have been tracking the application of real-time analytics to automated insight generation.

For now, though, the analytics trends are the ones which have demonstrated their utility and staying power.

While nothing is guaranteed in the tech world, even tech-averse companies can expect at least a reasonable ROI from their adoption.

Are you interested in adding to your business analytics toolkit? Get a full assessment of your BI needs!

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Leo Farias is the CEO and Co-founder at Concepta. He received his MPS in Business of Art & Design from the Maryland Institute College of Art. With over 18 years of technology-focused experience, he plays a vital role in architecting and leading various mission-critical projects for world-renowned clients like Time Warner Music, Orlando City Soccer, Vasco de Gama and Corinthians Soccer Club.