How Data Analytics can Empower Organisations of the Future
A research by McKinsey suggests that up to 30 percent of the hours worked globally could be automated by 20301. Indeed, AI is impacting every aspect of our lives - automating mundane tasks or simplifying complex work across diverse industries. However, unbeknown to many, a similar automation process is also happening in the data analytics domain - transforming the way businesses today utilise data toget ahead of the competition.
The organisation of the future is one in which all knowledge workers are empowered with data. Question is, how can businesses tap into the current trends and turn their use of data into a competitive advantage?
MAKING DATA ACCESSIBLE
Businesses must make the right data available to the right people. The IT team is traditionally responsible for providing data reports. With the increasingly common self-service approach in data analytics, their key role now will instead be to provide clean, governed datasets while ensuring everyone has access to the data they need. The proper creation and access management of company data is crucial because overly restrictive governance could indirectly encourage employees to generate their own data sources in spreadsheets, or download data from web sources. The use of such unsecured and uncertified data might pose a risk to the whole organisation.
On the other hand, with the selfservice approach in data analytics, the processing time of data reports which could previously take weeks has the potential to be significantly shortened. Furthermore, easyto-use visual analytics tools like Tableau offers a modern visual analytics approach to enable high usability across all skill levels, and give knowledge workers across departments and levels easy access to governed data. The result is, faster decision making and better results.
Case in point, Tableau helps online grocery retailer RedMart reduce report generation time by 70 to 80 percent. Its company-wide deployment also empowers informed decision making in a timely manner.
MAKING ANALYTICS SIMPLE
As AI advances, smart technologies are adopted to automate stages of the analytics process, lowering barriers to analytics and enabling more people to work with data. One such technology is natural language processing (NLP), which combines computer science and linguistics to help computers understand the meaning behind human language.
For example, the natural language feature - Ask Data - in Tableau allows users to ask questions as they think of them, without needing deep knowledge of analytics or the tool. By leveraging context within the conversation to understand the user's intent behind a query and further the dialogue, the system is able to create a more natural conversational experience. When a user has a followup question about their data, they don't have to rephrase the question to dig deeper or clarify an ambiguity.
Case in point, Tableau helps online grocery retailer RedMart reduce report generation time by 70 to 80 percent. Its company-wide deThey can simply ask, "What are our sales figures in APAC?" and then follow up with a direct question such as "How about in Singapore?". Users are therefore no longer limited by their analytical skill set - only the breadth of their questions. Advanced users can also get answers to more complex questions in less time, and all users can enjoy a dashboard with more engaging capabilities. It is foreseeable that as natural language matures across the business intelligence industry, analytics adoption will increase across organisations and further embed data into the core of workplace culture.
MAKING ANALYTICS INTEGRALHowever, as important as it is to inculcate a strong belief that data can and should play a crucial role in every business conversation and decision, one should be mindful that although everyone across the organisation can use data in some way, their requirements can be very different. Some might need the data for complex modelling and analysis, while others simply want to see the data to guide their decision making process. Companies should therefore invest in an analytics platform which caters to different requirements - and at a reflective cost - rather than opt for a one-size-fits-all solution. This will help put data in the hands of more people within the organisation - managers will no longer need to base decisions solely on experience and gut feeling, and the decision making process can be expanded to workers at every level.
With AI augmented platforms, it is increasingly possible to put powerful analytics capabilities in the hands of every worker, allowing effective and informed decisions to be made. Hence for businesses looking to survive and remain competitive, this transition is not a case of if, but when. Many leading companies have already embraced the modern analytics model - for the rest, the hard decision has to be made now, unless they decide to settle for trailing behind.
1 What the Future of Work will Mean for Jobs, Skills, and Wages, McKinsey, Nov 2017