Today, organisations are becoming increasingly data-driven. Business users now have greater than ever access to data & analytics technologies so that they can obtain current insights on their KPIs. The opportunity for businesses is to simultaneously provide support for self-service analytics, whilst ensuring security and integrity. Self-service analytics will encourage the promotion of a data-driven culture resulting in transparent processes & business decisions backed by numbers. However, not all business users are data literate and enterprises are still grappling with the challenges that make it difficult for enterprises to be truly data-driven.
Self-service analytics is a buzz in analytics today. The objective is to empower business users so that they can leverage data independently, thus reducing dependence on the IT/ BI team, all within a robust governance mechanism in place.
By defining the roles & rights within the data, organisations can also control and restrict the data and reports access for individuals belonging to different functions, departments and hierarchy.
How can organisations rightly position themselves to address the opportunities that arise from the emergence of self-service analytics & what are the risks that organisations need to avoid?
The Emergence of Modern Analytics
With self-service BI, the business user is empowered to access relevant data, perform queries and generate reports by themselves “ with the help of easy-to-use self-service tools. The key to a successful self-service analytics paradigm is usability- are the business users enthusiastic about it and whether they are well-trained in order to make optimal use of the platform. The answer to these questions will decide how well analytics is going to be adopted across the organisation.
Research firm, Gartner, coined the idea of a Citizen Data Scientist – business user trained with enough statistical & data engineering knowledge to execute the whole data pipeline from ingestion to processing to deriving insights and then working on these insights in order to make actionable business decisions.
The Gartner report defines a Citizen Data Scientist as a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics . Citizen Data Scientists will make the whole analytics endeavour more efficient and rapidly adopted. Today, with the available advanced analytics technologies and processes, it is possible to achieve this within organisations.
There is a huge onus on organisations today to make use of these available technologies & processes in order to become efficient and productive with the help of available enterprise data.
The right analytics partner can play a crucial role here in delivering an enterprise-grade & a robust solution by bringing in the experience of working across multiple domains & functions, and which will be readily adopted by the business user team leading to data-driven success. Within an analytics project, regular feedback from the users is the norm and multiple requests for dashboards, KPIs on the same data set come from the users of the applications. Managing these expectations within an evolving framework requires businesses to be agile with new data sources & evolving business requirements. When the initiative is taken up internally, the whole operation can become jeopardised and threatened by a lack of accountability from internal stakeholders.
Data Management
Data inconsistency & errors can undermine the whole analytics flow. GIGO – erroneous data input will result in faulty business insights, which might make the case for wrong business decisions – and which can be fatal for businesses. Hence data management is crucial for the success of an analytics initiative.
Limitation Of Business Users
Limitations in skill & experience to handle the data pipeline & process logic with the data can be typical with business users since they often lack strong mathematical or statistical background. In such cases, the analytics initiative can get undermined as a result of business users not being able to make use of the technologies. The organisation will have to invest heavily in regular training for business users. It will be very necessary since technology will continue to evolve rapidly.
Lack of Proper Governance & Security
Governance is critical to handle the enterprise data & any loop-hole here can wreak havoc & chaos within the organisation. Shadow analytics, a term used to refer to the situation when the workforce moves the data & analytics into ungoverned environments, sidestepping the essential controls that are put in place by the organisation. This tends to happen when the users are frustrated with the controls and restrictions that exist. For example, the user may download the data in a spreadsheet, make edits & calculations, combine them, upload it into their private cloud or analytics software on their desktop and use them for deriving insights.
This data is susceptible to breaches, malware attacks and other cyber-security threats which can exploit this vulnerability to result in massive losses for the organisation.
Hence, organisations need to marry the convenience of self-service data prep with the right control
With the right analytics partners who have the necessary experience & expertise in delivering the whole business solution around analytics, the whole process becomes easier. Businesses can control and tie them down to deliverables which will lead to significant cost savings & efficiency gains in the long run. At Polestar, we are an analytics partner of choice for enterprises, having delivered large-scale enterprise-grade analytics projects around the globe.
