Help Is On The Way: Use These Tools To Future-Proof Your Citizen-Analyst Teams

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Try as they might, cable companies can’t seem to catch up to the present, much less the future, of big data and the Internet of Things. They want to look ahead to new business that 5GL might bring, or the new marketing opportunities of location-based services, but they’re still losing business – right now – to more competitive business models.

According to eMarketer’s latest US pay TV/OTT forecast,
“The number of so-called “cord-cutters” (adults who’ve ever cancelled pay TV service and continue without it) will climb 32.8 percent this year to reach a total of 33 million subscribers.”

Building a customer-first identity that goes beyond the public’s “dumb-pipe” perception is just one part of the telecom challenge, though. The other part is getting up to speed with the digital transformation of business in general. As network and cable-service providers, they’re expected to be early advocates – and suppliers – of all things digital, to business as well as consumers.

They have to make new-technology decisions that work for tomorrow as well as today. But they also have to compete with their business customers to hire the best people, with the best skill sets, to work with the analytics solutions that will be required to make sense of the growing mountains of data that will be propelling business transformation for years to come.

Data scientists, business analysts and other analytics operators with strong skill sets are in great demand, but telecoms can’t wait for schools to fill that demand. What they must do is implement the analytics technologies that will make the best use of operators’ skills, and that will thus quickly expand the pool of available operators and analysts.

Here’s where they should be looking:

End-to-end process visualization

Everyone talks about the value of graphical interfaces and user-friendly dashboards. These are essential, of course, but what’s also needed are visualization tools that look across the organization, at complete processes, rather than just down into their own applications. These tools give users a complete, end-to-end view of their algorithms, so they can see how a change in one application may affect a different application. End-to-end visualization helps data scientists and business analysts see the larger context within which they’re creating analytical algorithms, and it helps them communicate with other analytics teams, comparing notes and insights for the betterment of themselves and the organization.

Quick-start templates

As analytics solutions gain maturity, quick start process templates are becoming more prevalent. Such templates, which contain menu-driven interfaces to model libraries, basic algorithms, data models and process rules, are useful for helping analytics teams create 80-percent-ready solutions – in days, rather than weeks or months – without having to learn arcane new programming languages.

Low-code extensibility

Also available are low-code development tools with point-and-click interfaces that simply the effort of turning those templates into working applications. Because the low-code tools are modular, they produce components that can be reused for other applications. Analytics teams benefit because they can turn their ideas into working prototypes successfully, which keeps them engaged in their team efforts. In some cases they can make use of existing code blocks, which cuts down on redundant effort, and gives data scientists and business analysts more time to think creatively about new solutions.

Open, standards-based architectures

Regardless of their background, chances are good that analytics team members are already familiar with Spark, Python, Tableau, and other industry standard tools. That’s why an analytics architecture should be compatible with industry standard languages, model libraries and other assets – and that incudes data warehouse and database standards that are likely to show up in company data stores or data lakes. Most of today’s architectures can claim to be standards-based; the key will be how well they track new standards as they come into play in the future.

Looking Beyond The Tools

Telecom companies have significant advantages as they race to implement digital transformation. They’ve got experience in keeping complex mission-critical services up and running. They’re used to working with big data – although not necessarily in real time. And they understand the sensor-level signaling that powers the Internet of Things.

Their challenge now is to build customer centricity into their day-to-day operations in order to move past the dumb-pipe connotation and into the lucrative markets for location-based and other end-user services.

Commitment Needed

Analytics tools can bring them a good part of the way by helping to ingest, analyze and act on millions of data events in real or near-real time. But what’s also needed is a commitment to customer-driven analytics from uppermost management. That’s the thing that will help empower analytics teams to do their best work and to form lasting kinship with the company.

Too often, today’s analytics projects evolve “from the ground-up.” They may or may not have the blessing – or the budget – from top management to function as well as they could. And because they didn’t start with upper management, they may not be seen as strategically important to the organization.
In contrast, top-down commitment to analytics can go a long way in helping everyone involved work together toward a clear goal.

Talking about self-service analytics, Gartner has said, “Self-service analytics initiatives can easily flounder or fail if business users and IT staff distrust each other and do not work collaboratively to deliver results. This distrust can be grounded in simple bias or prejudice. Often, it’s grounded in assumptions, often bad, about each other’s values, attitudes and actions.”

It’s A Journey

All types of companies stand to benefit from the democratization of analytics, which will put creativity into the hands of those – the business analysts and data scientists themselves – who understand the inner workings of the business. The analytics tools available today are more than ready to help, even in the face of the coming onslaught of IoT data. What needs to happen, then, is for upper management to realize and act on the vital strategic importance of analytics, and analytics teams, in the transformation journey.

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