While organisations recognise the potential of data, many struggle to turn data into actionable insights that provide value

According to a recent Alteryx-commissioned IDC Info brief, The State of Data Science and Analytics, there are approximately 14 million data workers in the Asia Pacific and they spend 80 per cent of the workweek on data-related activities.

Yet, 45 per cent of this time is wasted each week because data professionals are bogged down by the sheer scale, complexity and diversity of their organisations’ data.

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A huge problem revolves around the way many organisations have siloed data departments away from the rest of the organisation, limiting the way data can be used.

It is an antiquated thought model and one that needs to be changed in the age of Big Data as organizations continue to generate what seems to be untapped goldmines of data sources across industries and internal functions.

So how can organisations bust down the old siloes around building an analytic culture? How can they finally realise the potential of data analytics to achieve higher ROI?

These are questions that many organisations are grappling with as they think about either establishing a data science team or empowering existing team members to become data scientists.

 Empowering business analysts to be citizen data scientists

To start, organisations need to empower data professionals on their team, regardless of technical acumen, to become more data literate and to improve their analytic knowledge.

In a workforce that is becoming increasingly data-centric, there is not enough expertise to keep pace with the explosive demand for data scientists.

Data scientists come at a premium, are difficult to retain and are few and far between, making it challenging for enterprises to build out this technical capability.

Furthermore, organisations need to recognise that not every data-related job requires a data scientist with advanced qualifications.

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Instead, the key is to empower business analysts sitting in various lines of business, many of whom are currently stuck in spreadsheets, to analyse data more efficiently and drive real, measurable, business results.

Coined by Gartner, the term ‘citizen data scientist’ is defined as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.

With the power of a code-free platform behind them, a citizen data scientist (CDS) can be up-levelled to create data and business models with the help of data analytics tools, even if they do not have advanced mathematical expertise or technical skills such as writing custom codes.

It’s not rocket science. Creating and empowering citizen data scientists is a unique opportunity to develop existing talent who know an organisation’s data and understand their business priorities and objectives.

These professionals know the business inside and out and probably have a laundry list of ideas they aren’t empowered to tackle. Imagine then, the power of unleashing their potential and the type of quantifiable business impacts they could realise.

Often, they are eager to learn and develop skills to improve their personal development and contribute to the business, thus, creating a virtuous cycle of improvement.

Providing self-service tools that put humans at the centre of analytic intelligence

As the data and analytics landscape becomes more complex and fragmented, it is more pertinent than ever to democratize access to analytic tools among members of a business who can best create and derive value from the data.

The rise in the use of self-service data analytic tool has become so pronounced that Gartner predicts that by the end of 2019, the analytics output of business users with self-service capabilities will surpass that of professional data scientists.

Going beyond the implementation of self-service capabilities, business analysts and citizen data scientists must be encouraged to ask tough questions and be provided with the necessary training to answer these questions, to ensure the success of a self-service approach.

Today, there are even self-service data analytic platforms that have taken it one step further to put more advanced analytic capabilities into the hands of business experts.

These are people who know their company’s business and data and are in the best position to assess whether a prediction that can impact the business – but do not have technical skills to write codes and deploy predictive models.

To help them advance their skills and harness the advantage of AI, self-service platforms now come with a guided walk-through to build machine learning models in a code-free, drag-and-drop environment.

Data is at the core of digital transformation, and this trend is driving demand for analytics across all business functions of an organisation.

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Building this analytic mentality and creating a platform that embraces teamwork and model building will be pivotal in developing the next generation of data workers. One that thrives on making self-service analytics your competitive advantage.

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