By 31 October 2016 | Categories: Press Release



By Rudraksh Bhawalkar, Practice Manager, Analytics, Africa, Wipro Limited and Dean Terry - General Manager, Analytics, Wipro Limited 

A company’s data is now firmly acknowledged as one of its most vital assets. With a better understanding of one’s data, organisations can move closer to their customers, decide how best to evolve and transform their operations, and enhance their offerings.

Many executives are realising that now is the time to start defining their analytics strategies, and start building the tools that enable their organisations to make sense of the masses of data streaming in.

But in South Africa, we still see a pervading sense of inertia – borne from a number of challenges which we have encountered based on our engagement with multiple clients:

•             Existing legacy IT infrastructures are not designed to handle the workloads of analysing large volumes of increasingly complex data

•             Building in-house analytics engines is often a very capital-intensive and time-consuming process

•             Data science and related technical skills are in short supply in South Africa, making it difficult to stock the right team, and retain skilled individuals

The answer to these challenges could well lie in the rapidly-evolving sphere of Analytics-as-a-Service.

Using Cloud-based tools specifically designed to ingest and analyse large data sets, CIOs and their teams are able to get their data analytics engines up and running at rapid speed.

Instead of worrying about building the tools, they’re able to focus all their time on deriving value from the data – transforming the business based on the insights that are generated.

With Analytics-as-a-Service, one can scale and customise the tools as the business requires – and build out machine learning and artificial intelligence capabilities over time, continually enhancing the process of data analysis.

In fact, Analytics-as-a-Service enables us to realise the vision of “Big Data 2.0”: a centralised, consolidated platform, on which applications from every point in the value chain can reside. Each application is able to automatically draw insights from the platform.

So, as organisations set out on the road to Big Data 2.0, where should they begin?

Firstly, it’s important not to fall into the all-too-common trap of ‘doing analytics for analytics’ sake’. A better starting point is to consider the boardroom-level priorities, and determine which data sets are most likely to add value to these key strategic objectives.

Allied to this is the focus on customers, and their experiences of your organisation. Ask yourself what are the customer journeys and customer experiences you’d like your users to enjoy? And what data will you need to collect in order to know how to improve those experiences?

With these principles at the centre of your thinking, the next step is to select an enterprise-class, highly matured Analytics-as-a-Service platform. Ideally, it should provide you with pre-packed ‘accelerator’ services that allow you to derive instant results from the platform.

From there, you can start building greater levels of sophistication into your data analysis, as you use the initial insights to start making changes to the business (in line with the executive vision that’s been outlined).

We find that organisations start very quickly discovering new uses for their data, enabling them to see fresh market opportunities, provide new services to customers, and streamline efficiencies within the organisation.

But this applies not just to large enterprises. Excitingly, Analytics-as-a-Service can help newer, more disruptive companies compete against their larger peers, both locally and globally. For the first time, it’s no longer just the ‘big boys’ that are able to afford enterprise-class analytics tools. For Companies of all sizes, there are wonderful opportunities to take advantage of ‘Big Data 2.0’.

Instead of being paralysed by over-analysis of the strategy, organisations should dip their toes in the water, and get started on their Big Data journey, by effectively using latest-generation Analytics-as-a-Service platforms.

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