It would be a bit of an understatement to say that we live in a data-rich age. To illustrate, sample the following data released by Cisco, in a whitepaper titled, Cisco Visual Networking Index Forecast and Methodology, 2015-2020,

  • Annual global IP traffic will surpass the zettabyte (ZB) threshold in 2016, to reach 2.3 ZB by 2020.
  • Global IP traffic will increase nearly threefold over the next five years, and will have increased nearly 100-fold between 2005 and 2020.
  • Smartphone-based traffic will exceed PC traffic by 2020, to account for 30 per cent of total IP traffic. (PC traffic will account for 29 per cent).
  • Globally, mobile data traffic will increase eight-fold between 2015 and 2020.
  • The number of devices connected to IP networks will be three times as high as the global population in 2020.There will be 3.4 networked devices per capita by 2020, up from 2.2 networked devices per capita in 2015.

Net, net, it would be safe to assume that the flow of data-based traffic isn’t going to abate (anytime soon, in any case). But, what’s an operator to do with this barrage of facts and figures? These numbers will, of course, come in handy, especially given the fact that operators are fire-fighting on several fronts simultaneously. (I allude to the threat of over-the top players, razor-sharp competition and wafer-thin margins). Here’s the catch, though-these numbers will remain mere massive repositories of unstructured data, unless the operator deploys tools that would help them sift through the pile to uncover actionable insights about the customer’s behaviour.

Here’s where big data analytics steps in. Very briefly, today’s customer demands constant engagement with any brand. Things become even more complex, as we live in a multi-channel world. Companies thus need to think on their feet to provide an optimum level of customer experience management. Why? Well, to ensure that the customer is constantly engaged and is able to interact with them across multiple screens. Big data analytics thus helps companies to constantly innovate and make fast and streamlined business decisions, in real-time, of course!

So, isn’t the decision to deploy big data analytics in one’s business a no-brainer? Interestingly, no. Here’s why-most companies are extremely agreeable to deploying analytics to give an edge to their business. What trips them up is the debate on whether to build an in-house team for the same (too complex and time consuming) or to outsource the entire gamut of activities.

In fact, analytics as a service can help in a multitude of areas, which are not only limited to the team of experts who manage a set of tasks for an operator. There is a lot more-such as tools, security, storage, system integration capabilities to integrate new and old nodes and solutions to make this entire methodology more holistic. All these requirements can be bundled under the managed services providers’ scope of work. Apart from this, the managed services provider can also help to identify the appropriate action required (through tools or otherwise), after analysing the huge pile of data flowing through the network.

Of course, the argument isn’t that stark. According to Syntelli Solutions, a few key factors driving the argument in favour of the latter include:

High demand for a precious few data science practitioners– Typically, companies would rather turn their attention to solutions like managed analytics, than comb the markets for this scarce resource.

Exorbitant prices and equally exorbitant risks-Companies usually deployed analytics services through fixed-price or testing and measurement consulting models. As a result, the prices and risks associated with that project rose significantly. The solution? Begin relying on third-party analytics via the subscription model, of course!

Enter the Cloud. Today, cloud-based services are becoming increasingly reliable and secure. And this is just the beginning. It goes without saying that any future improvements in cloud-based processes will drive managers to a less expensive, more reliable managed service model.

It is little wonder, then, that operators are increasingly opting to outsource their managed analytics-centric activities to a managed services provider. Shall we take a closer look at the benefits this shift will accrue?

  • Managed analytics services provide the necessary push for an organization to move from a capital expenditure to a predictable operating expenditure cost model. Why predictable? Well, isn’t a set of easily definable results an excellent business case to put before the higher-ups in the organization?
  • Advanced tools and coherent and tested processes are deployed by the managed service provider. This, naturally, not only makes the service itself more outcome-driven, but price can be tied to quantifiable business value-extracting meaningful insights into the customer’s mind!
  • Organizations can achieve faster time-to-market. This is because managed analytics services cuts the internal process of building an analytics team by half (no hires, no systems to implement).

This, in a nutshell, is why a company ought to gravitate towards managed analytics services. On a parting note, I would like to add a brief caveat-operators would do well to remember not to get carried away and outsource all their analytics-related requirements. This is because the type of value this tool can provide varies from company to company. In all probability, each company already has a certain level of managed services implementation. The trick is to simply integrate and enhance the existing set-up. So, what’s your managed analytics services strategy?