The ABCs of Analytics and BI

The ABCs of Analytics and BI

The increasing need for analytics and business intelligence platforms for organisations is placing even greater stress on creating user and data-friendly tools. What are the key features of a good ABI platform?

Analytics and Business Intelligence (ABI) platforms today are absolutely crucial when it comes to firms engaging in data-driven decision-making. It becomes especially relevant for non-technical data users, providing them self-service access to data, visualisations, analysis and reporting that is intrinsic in deriving insights and furthering the firm’s growth. Getting the right tool is unequivocally crucial in this regard. Consulting giant Gartner writes:

“For buying teams researching and evaluating ABI solutions, some of the most important requirements in selecting a platform will relate to how it functions – that is, whether it provides the capabilities the organisation needs to meet its specific objectives. This is only part of the rigorous process that buyer teams need to evaluate vendors and their solutions; other considerations include technical interoperability, the availability of support and services, vendor health and ultimately pricing and commercial terms. But the functional requirements are the first and most pressing criteria.”

Keys to a good ABI platform

Business Intelligence platforms are primarily those which allows firms to further their BI applications by facilitating three major aspects: analysis (such as online analytical processing, or OLAP), information delivery (in the production of dashboards and reports) and integration (such as in producing a development environment and appropriate BI metadata management). These can be regarded as the primary functional requirements for these platforms – and judgement on the efficacy of the tool needs to be judged against these three criteria, and how much the tool can be personalised to the firm’s needs. Some of the aspects to take care of, in this regard:

  • Automated insights:A good ABI platform today must be able to integrate machine learning algorithms to automatically generate actionable insights. Of course, this will only get better with time as more data is fed into the algorithms, but initial aspects such as the identification of key attributes need to be features present in the analytics engine of the platform.
  • Data preparation:Gartner writes, “Can the platform combine data from different sources using a drag-and-drop interface and create analytic models based on user-defined inputs, such as measures, sets, groups and hierarchies? After usable data has been defined, the tool should allow users to effortlessly combine datasets from approved sources and customise insights based on inputs they define.”
  • Data visualisation:The platform must provide capabilities to support the production of highly interactive dashboards and data exploration through the manipulation of charts, and go beyond the scope of simple bar-, line- or pie-graphs to provide greater flexibility in data presentation. Tools like Datawrapper or Tableau provide said options.
Image: Multiple chart types in visualisation tools such as Datawrapper are imperative;
Source: Datawrapper

Product management and usability: The ABI tool in question must be able to adequately share information among concerned parties, promoting both individual and cohort usage. The interface of the tool also needs to be user friendly and easy -to-understand, thereby facilitating both user engagement and broader adoption. Gartner writes:

“If users feel intimidated or overwhelmed by an ABI platform, they won’t adopt or standardise workflows that incorporate its capabilities. In organisations that also prioritise change enablement, ensuring high product usability should be a top priority.”

On the backend, technical requirements such as technological setup and delivery are essential to choosing the appropriate ABI tool. Data storage is crucial in ensuring the tool provides storage capacity, file types and location, especially in processes such as extraction and eradication. Data integration is key as well: it must gel well with data sources, applications and other technologies. Monitoring, logging and tracking, providing proactive alerts on system events such as logging, and resolution reporting become important in this regard.

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