Team Up For Customer Service Excellence

The concept of business intelligence has come a long way in a few short years. From proprietary APIs and query languages to data warehouses and, today, self-service Internet of Things analytics, the notion of finding hidden, game-changing secrets in piles of unsorted, uninterrogated data has captured the imagination, and emptied the pocketbooks, of companies from A to Z.

Why? Because those tidbits of insight can in fact be game-changing for those that are first in their industry to find them. Today we have cloudbursts of IoT data, and we have powerful technologies to analyze that data. And we have self-service tools for business units to find the answers they’re looking for without delay.

Much has changed. But one thing that doesn’t change is the need for people – data scientists, business analysts and business unit managers and others – to get the most value possible out of those new tools. To do that, today’s analytics teams need to possess the right combination of skills for their jobs and their industry.

Finding and cultivating those skills can prove challenging, since it’s less than clear what new IoT applications and analytics technologies will be in play in the future. But one thing is certain: for most, success will come by keeping a strong focus on customers and customer service, since it’s customers who, ultimately, pay the bills.

In that spirit, here’s what to look for in your own IoT analytics team:

Data Scientists

The term “data scientist” actually covers a range of skills, and even nomenclatures, including data engineer and machine-learning engineer. New skills seem to be added daily because of the fast-evolving nature of analytics techniques and technologies, so the best candidates for an analyst team will be those whose skills match most closely with the specific needs of the business and its project scope. Skills to watch for range from knowledge of statistics, programming and even software development to machine learning and data visualization.

Beyond technical skills, the data scientist should also be willing and able to convey his or her technical knowledge to business analysts, unit colleagues and managers, and should be comfortable, enthusiastic even, about intuitive problem-solving.

The IoT analytics team will likely evolve to members who can blend the qualities of business user and data scientist to function as what Gartner calls “citizen data scientists.” Also, professionals of varying types and backgrounds will serve, either full or part-time, to bring their own value, and a variety of perspectives, as the team’s sophistication grows.

Business Analysts

It’s obvious that a business analyst should be steeped in domain knowledge as well as knowledge about your company, its competitors, customers and stakeholders.

For instance, for the telecom industry, an ideal candidate will be sensitive to the needs and differences between traditional telephony operations support systems, or OSS, and the more customer-focused business support systems, or BSS. Recently, telecoms have been merging the two cultures together. Putting in place IoT analytics that pay full attention to customer needs and future likely scenarios will be a good step forward.

The business analyst should therefore want to be in regular contact with customers and customer-service contacts, and with other, customer-facing business units, as much as possible. Self-service or not, the best analytics implementations will be closely aligned with – and likely sharing some application components with – analytics colleagues in other business units.

Finally, the business analyst should be enthusiastic about communicating and sharing ideas with other team members, and about learning from the data scientists and other team members.

Making Connections

The core team should also be comfortable calling on co-workers, and even outside experts, to add to the group’s knowledge and experience. Field service and finance are obvious choices. They can add unique perspective, and serve as sounding boards based on their own areas of expertise.

Most important, any analytics team, regardless of business unit, should cultivate an active partnership with the IT group. Deploying an IoT analytics initiative carries an inherent risk of creating chaos and confusion both within the business unit and across the organization.

IT can help prevent this by centralizing governance and security, and by helping maintain consistency of data definitions and reporting formats. Without some level of standardization, one business unit’s analytics reports might use metrics that are inconsistent with another’s. Put together in a presentation to the board, the results could be inaccurate, misleading, or even embarrassing. Hence the value of central coordination by IT.

An Analytics Framework

Business units and their analytics teams should also employ IT to help with the selection of an analytics framework, or architecture. Using a single, end-to-end architecture creates a stable foundation for unit-by-unit analytic applications and pursuits. It can coordinate access to the company’s various data warehouses, databases, data lakes and other data stores. And it can promote reuse of analytics software components, something that will add to overall efficiency and reduce conflicts among application developers of different business units.

With its own in-house technical expertise, IT can help in reviewing the machinery behind the analytics framework – the quality of the engine, which will certainly be cloud-based; the utility and quality of application development tools and frameworks; and the abilities of the models and machine-learning technologies to create a strong customer-focused dimension for your diagnostic, predictive and prescriptive algorithms.

Thanks to IT, and to your own best efforts in assembling and developing the analytics team, you’ll be well positioned for success. Your team will be enthusiastic, ready and able to concentrate on their core analytical tasks. They’ll know who to contact for advice and help, and their applications will reflect a well-considered approach to the next generation of IoT analytics.

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