Which Business Analytics Trends Can Be Put To Use Today?

April 6, 2017 4:28 pm Published by

business analytics trends

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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 promise 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. Within two years the world will be spending $2.2 trillion on digital transformation initiatives annually, a 60% increase over 2016 numbers.

Building up digital infrastructure costs money, and choosing the right technology ensures a smooth return on that investment.

A number of business analytics trends are already picking up speed in 2017.

Some are years away from being able to deliver on their promises. Others have reached the stage where a company can gain a competitive edge by using them while side-stepping most of the usual 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.

Embedded analytics

Basic reporting has been a feature of software for decades. Every accounting and management program has an option to download its data as a spreadsheet.

Embedded analytics takes things farther, providing not only access to data but an approachable platform to to interpret it.

Everyone who has used a modern enterprise application has been exposed to embedded analytics, whether they realize it or not.

The term refers to advanced reporting software which is integrated into another piece of software (marketing, social media, etc) so completely that a consumer experiences it as a single tool.

As Business Intelligence vendors compete for dominance in the growing enterprise software market, embedded analytics are increasingly seen as the standard rather than a bonus feature.

Users expect to be able to view and analyze their data on a dashboard without exporting it to an outside program.

Because of this convenience, Nucleus Research predicts that 90% of enterprise analytics tools will be embedded in other software within five years.

The technology has grown past its trial phase into a comfortable state of invisibility. Users don’t notice its presence anymore so much as its absence, which makes it a very safe bet for investment.

It also supports another enterprise-ready trend: predictive analytics.

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.

There’s still a long way to go before the full potential of predictive analytics is realized, but its current capabilities are more than mature enough to justify its adoption.

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

The benefits of predictive analysis are well demonstrated by its application to 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 notably 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 (additional training, more appealing benefits packages, transfers to more engaging positions, etc.).

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

Real time streaming analytics

Streaming analytics give enterprises a living visualization of their operations through a central dashboard.

It’s a major trend for 2017, with the market expected to reach $2 billion in the next three years.

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, like offering location-based services to travelers and pushing sale alerts to website visitors.

The information is usually displayed in a format that doesn’t require a data science degree to understand, too, which makes it easy to act on quickly.

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 development users generally know they’re speaking to a chatbot, though 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 verbally request analytics and services and receive replies either out loud or projected to a specific device.

Recent advances have allowed virtual assistants to communicate with a huge variety of popular enterprise programs, even letting them complete purchases using pre-approved sites.

These analytics trends have demonstrated their utility and staying power. While nothing is guaranteed in the tech world, enterprises who integrate them can expect a respectable ROI. 

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