The area of big data has gained vast amounts of attention over the past few years but the end goal is to in essence help make businesses to become data driven.
Becoming a data driven business though is not simply achieved overnight, it is not attained by deploying an analytics tool or platform, or by just devising a data strategy, there are numerous elements that need to be considered.
Simply gathering data isn’t where the value lies, it lies in the information that can be gained from the data.
Information is only useful when it is giving you insight into your business – and providing you something you can act upon.
While it is a complex process there is help being provided by vendors that can be an essential tool in transforming the business.
Hewlett Packard Enterprise for example has created a Data-Driven Organisation Transformation Workshop that will help businesses to formulate and execute a strategy. This kind of workshop provides a footing from which businesses can start from, helping to identify potential challenges in transforming the traditional IT operation.
Starting with a clear vision of the desired business impact is something that should be one of the starting points for any business going on this journey. This can help to shape the integrated approach to data sourcing, model building, and organisational transformation.
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The organisational transformation piece is one of the main stumbling blocks when it comes to changing a business and the culture element shouldn’t be underestimated.
Research firm Gartner believes that getting started with analytics isn’t just about getting the right tools in place – a change of mindset is also needed.
The idea is that if a change of mindset happens then there is a greater chance that big data projects will succeed. Gartner predicted that 60% of big data projects through 2017 will fail to go beyond piloting and experimentation and will be abandoned.
For an area which has received so much hype to have a 60% failure rate is pretty damning but there has been strategies devised to increase the chances of success.
First of all it is necessary for a business to choose a problem that offers an initial win, so basically don’t start with the hardest problem, becoming data driven takes time and is a process learnt. Working with business leaders helps to identify the problems to tackle and aligns them with IT so that they are a part of the process from early on.
Depending on the maturity of the business it may be necessary to outsource and buy packaged applications when lacking in advanced analytics expertise. This is because building these capabilities in-house can be a labour and time intensive job.
Getting stakeholders’ on-side is one of the most important factors that must be considered for a transformation to be wholly successful, it is a job half done if several lines of business aren’t data driven even if the rest of the business is.
This comes back to building the business case for data use so that the value of the project can be demonstrated.
The final part is deciding whether or not to build the skills and internally, research firm Gartner said: “It makes sense for an organisation to build advanced analytics internally if (a) analytics is a critical differentiator in its industry or if the area is of strategic importance.
“(b) A high level of agility and granularity of control is required, and (c) there are many opportunities across the organisation to apply analytics in multiple use cases or lines of business.”
For businesses looking at the higher end of data analytics, the data scientist, it may be necessary to hire the right staff and then support them with either in-house produced tools or look to vendor tools.
Gareth Martin, Head of Analytics & Data Science EMEA, HPE, told CBR that each company has a different motivation but that it is necessary to map out what the strategic vision is.
The growing importance of big data to businesses has seen a growing demand for skills in order to successfully make the most out of the data, one of the most in-demand jobs is that of a data scientist.
The size of the data science team depends on the use case, Uber for example has a small team, but is a data-driven company.
Although there is a huge demand for data scientists there is a clear skills shortage but vendors have been increasingly proactive in developing high quality tools to try and bridge the gap and to reduce the complexity these tools are often available in a platform.
There is no point collecting and storing all this data unless you can make use of it – this relates back to the Gartner suggestion for outsourcing, or seeking assistance from an outside source.
Data science is a difficult and specific set of skills. But there are various dashboard based systems which help take some of the grunt work out of crunching the numbers.
A fatal error for many big data project is useability. Whatever you create must be simple enough for the right people to use it. They will need some training of course but the more open your systems are, and the more people can access them, the more likely you are to gain real insights.
Becoming a data driven business like an Uber isn’t going to be achieved overnight, it is a process that will take a combination of strategy, tools and services, and often outside help in order to be a success.
Beyond the tools it is equally important that higher levels of management are open to listening to the insights gained.
Understanding what the desired business outcome is and having a strategic goal will help to align the business while assessing tools and services in order to choose the right one will help with choosing the right one.
Although no silver bullet exists to suddenly become a data driven success, the rewards of getting it right will help to position your business in the right place for the future.
