Big data and analytics are being integrated into organisations in many different ways, from internal operations to driving the business model.
An example of that could be such initiatives as Facebook advertising and news feed algorithms.
This is not only creating opportunities for the enterprises to target previously untapped markets, reduce operational costs and improve the communication strategy, but also introducing new roles and responsibilities that may not have even existed a decade ago.
Although many companies are starting to embrace the possibilities provided by these technologies, they experience varying success.
>See also: Top 8 trends for big data
The Big Data Payoff report by Capgemini and Informatica found that less than a third of companies have implemented profitable big data initiatives.
This means that companies will go through a range of changes in order to fully capitalise on the benefits of big data.
Emerging new jobs and roles in Big Data
The increasing importance of big data and analytics is disrupting the traditional C-level roles within organisations.
This has become very apparent through the recruitment of chief data officers and chief digital officers (CDOs) whose responsibilities include managing all data and digital transformation work, and making sure that such projects are aligned to their company’s strategy.
While such positions are still relatively rare, Gartner predicts as many as 90% of businesses will expand their executive boards by adding a CDO by 2019.
In addition, businesses are expanding their teams by hiring a range of specialists, most notably data scientists, visualisation experts, and data analysts.
Being able to visualise big data is another challenge in itself. It requires a good grasp of technical IT skills as well as being able to translate numerical figures into meaningful visual representations.
The final data visualisation products may range from descriptive reports to exploratory analytics projects used as an aid to understand datasets, but what they have in common is the ability to convey a story in an easy to understand and influential way.
Furthermore, these roles are being integrated into all departments in a business, rather than in dedicated analytics teams. This distinguishes them from the traditional IT analytics roles where deep business knowledge is not necessarily required from the specialists.
Businesses are moving towards a data centric framework, while decentralising analytics.
One of the most successful examples of that could be data analytics employed by Uber. Consolidated journey and traffic information is used to regulate live pricing, supply of services, and give ratings to drivers and clients alike, thus standing in or supporting the traditional operations, sales, and HR functions.
Big data implementation
All of these developments are well and good but they need to take into account how the data is integrated within the wider company’s structure. One of the most common yet crucial challenges is creating a central system where data is acquired and stored.
While decentralising analytics teams may be beneficial in order to tackle specific business challenges, it is important to acknowledge that different parts of an organisation will often have their own KPIs and goals to achieve, which introduces a multitude of ways to interpret the data.
Even if a lack of coherence is unintentional, it could impede the efforts to drive the business towards a unified goal. Furthermore, it risks duplicating resources and increasing project costs which could be better spent on improving data quality.
Therefore it is integral to have a common data access and reference point in order to ensure it is consistent while being used across the organisation.
Data management, quality and security, together with constrained budget, are among the main areas of concern for companies when dealing with big data.
While department-specific analytics programmes can affect a business functioning internally, insufficient security measures could cause more serious harm to the business by affecting its customers, damaging the brand name, or even earning large legal fines.
The focus on security is very well justified when looking at the aftermath of serious data breaches, such as the recent TalkTalk breach where hackers managed to get hold of customers’ card numbers (although not enough information to actually use them for purchases).
Key success factors
We have now highlighted the key impediments to make big data and analytics work in a profitable way. However, solving these challenges may require a top-down approach with significant buy-in from senior management.
Their involvement not only helps to define a clear data strategy but also to guarantee the necessary financial and operational resources in order to tackle the security concerns and integration issues.
It is therefore not surprising that about a third of profitable projects are led by COO rather than any other executive role, suggesting that the overall integration may be more important than the technical knowledge of individuals.
The past few years have seen an explosion in the amount of information available to enterprises as well as new inventive tools to analyse it. While some technologically savvy companies are making good use of these opportunities, many are not running their data projects in a fully profitable way.
In the future, these companies will need to change their corporate structure in order to be able to drive the most value.
Senior management needs to understand the value of big data projects in order to make sure projects are well suited to the company’s strategy. This has to be underpinned by consistent, secure, and high quality data governance.
Change needs to happen from the top to the bottom of the organisation in order to fully embrace big data.
Sourced from Agne Skrebyte, consultant, Capgemini Consulting