A 2023 Primer on Data Analytics

A 2023 Primer on Data Analytics

Part IV: Automation, AI and best practices

This is the fourth and final article of a series on where data analytics stands today and what to look forward to in the coming year.

Also read parts I, II and III for the types and key models.

While most organisations today realise the value of data analytics in their business operations, most are yet to achieve complete operational maturity. In this regard, US tech research and consulting firm Gartner has detailed five levels of maturity:

  • Basic: The initial maturity stage where data and analytics efforts are managed in data silos, focusing mainly on backward-looking events. This uses transactional logs and data. Here, however, analytics processes are performed on an ad-hoc basis and there is little governance or automation – with analysts having to deal with large spreadsheets and large volumes of data.
  • Opportunistic: At this stage, organisations look to focus on broader data availability requirements for various business units and the setting up of parameters to ensure appropriate data quality. However, as tech news conglomerate VentureBeat writes: “all these efforts remain in silos and are affected by culture, lack of suitable leadership, organisational barriers and slow proliferation of tools. The data strategy also lacks business relevance.”
  • Systematic: Although data is still not a key business priority at this level, executives become a lot more adept at data and analytics with a clear strategy and a focus on agile delivery. Data warehousing and business intelligence capabilities are adopted, and data handling becomes more centralised.
  • Differentiating: This is an advanced stage, where data starts being treated as a strategic asset, linked across business teams, serving as an indispensable tool in pushing for growth, performance and innovation. There are Chief Data Officers leading analytical efforts and measuring ROI while executives ‘champion and communicate best practices.’ The use of AI/ML, however, is still limited and governance gaps remain.
  • Transformational: Organisations at this level implement data and analytics as a core aspect of business strategy with deeper integration of AI and ML and data influencing almost all the organisation’s key business investments.

According to former Gartner VP and analyst Nick Heudecker, “Organisations at transformational levels of maturity enjoy increased agility, better integration with partners and suppliers, and easier use of advanced predictive and prescriptive forms of analytics. This all translates to competitive advantage and differentiation.”

Data and analytics best practices

Here are certain aspects to keep in mind:

Improving coordination and setting a clear objective:Before bringing in people with tools and technologies to improve an organisation’s analytics processes, coordination between people and processes becomes very important. Part of this improves breaking down silos and promoting data centrality.

After data has been adequately centralised, there should be clarity regarding what has to be done with that data – goals should be clear and be made a priority to ensure resources are deployed in the best possible way, to maximise ROI.

Scalability, audit and compliance: When a data analytics tool is being chosen at the organisational level, it is crucial to ensure the tool continues to deliver at high volumes of data, analytics depth and increasing number of users.

On audit and compliance, VentureBeat opines: “Organisations should conduct an audit of analytics-critical capabilities, including: the ability to measure performance metrics as per set goals, the ability to create predictive models, and the quality and completeness of the data needed. It’s also important to connect compliance with data analytics. This can help you make sure your users are following government rules and industry-specific security standards when dealing with confidential business information.”

Model refining and performance monitoring:Organisational models used in analytics need to be kept dynamic and upgraded regularly over time. One also needs to remember that data too can get stale over time, leading to issues with model performance. Regular monitoring is crucial. To exploit capabilities and maintain competitiveness, firms need systems to support enterprises’ data science and data and AI engineering teams.

Standardised reporting and data storytelling:Focusing on standardised report-producing tools across the firm ensures reports and visualisations produced look similar to all users, irrespective of department. Multiple reporting formats lead to confusion and erroneous interpretation.

Focusing on data storytelling as well as visualisations using tools such as Tableau allows every business user, including those without heavy analytical skills use insights for decision-making.

[Concluded]

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