To make the most of opportunities presented by AI, organisations have to tap the well of power that is their data. But to do so, they need the right governance, skills and structure to work more closely with their data and reimagine their approach. In order to become a more data-driven enterprise, and establish data foundations capable of powering innovation, forerunners in this space are adopting data analytics at scale. 

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Data analytics allows teams to get to grips with the nuances of their data, and find new ways to get value from it. From a closer analysis of customers to a more informed understanding of risks and access to actionable insights, an embedded data analytics capability offers a number of avenues for transformation. 

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In this blog, I’ll explain what data analytics is, what it (importantly) isn’t, and how enterprises can use this to become closer to the data and capable of capitalising on opportunities. 

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What is Data Analytics and Why is it Important?

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Data analytics is intrinsically tied to working with your data and gaining a better understanding of your ‘data reality’. By understanding where your data lives and how it works and operates, you can bridge the gap between technology and the wider business. 

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Within an organisation or business domain, you will have commercially minded decision makers and subject matter experts, and you will also have data engineers and data scientists. Data analysts will allow you to connect the dots between these technical and commercial minds and allow them to speak in each others’ language. 

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Utilising data analytics can also help surface and define the metrics that really matter. In order to go beyond throwing numbers into charts, data analytics equips you to find meaningful patterns in the data and inform decisions across the business that can be articulated across the organisation. 

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What isn’t Data Analytics?

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Data analytics isn't about managing data platforms or the technical intricacies of data engineering. While SQL skills are often essential for querying vast datasets, data analytics isn't synonymous with any specific technology. Contrary to popular belief, it's not solely about artificial intelligence or machine learning, which can even be considered as separate domains with different roles & skills entirely.

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Moreover, data analytics extends beyond merely shipping visually appealing dashboards. It's not about creating dashboards that are aesthetically pleasing but lack real utility or insight. Similarly, it's not about chasing after vanity metrics or finding numbers that confirm preconceived notions.

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At its core, data analytics is about extracting meaningful insights from data to drive informed decision-making. It involves analysing data to uncover patterns, trends, and correlations that might not be immediately apparent. It's a process that requires critical thinking, statistical analysis, and a keen understanding of the business context. Ultimately, data analytics empowers organisations to make data-driven decisions that lead to improved efficiency, innovation, and competitiveness in the digital landscape.

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What are the Core Elements of Data Analytics?

The core elements of data analytics encompass exploratory data analysis, where raw data is scrutinised to understand its characteristics and uncover initial insights. 

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Business analysis involves aligning data findings with organisational goals and objectives, ensuring that analytical efforts are strategically meaningful. Visualisation plays a crucial role, translating complex datasets into intuitive graphs and charts for easier comprehension. 

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Analytical techniques varying from simple cohort analysis through to machine learning techniques are employed to extract deeper insights from the data. Additionally, data storytelling involves effectively communicating these insights to stakeholders through compelling narratives, ensuring that data-driven decisions are made based on a clear understanding of the findings.

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Together, these elements form the foundation of data analytics, enabling organisations to leverage data as a strategic asset for informed decision-making and driving business success.

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The Main Types of Data Analytics

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There are specific techniques & approaches for analytics in different domains:

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• Marketing: Understanding where to spend your next marketing dollar is increasingly complex with more emphasis on privacy meaning user activity is harder to track across different applications/devices.
• Product: There are a wealth of tools & techniques dedicated to understanding how users engage with products so user journeys can be optimised.
• Wider domains: Some analytical techniques apply in more than one domain, but almost any of these functional areas has specific technology & data associated with it, and requires deep domain knowledge to drive real impact as a data analyst.

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Data Analytics Sounds Important, What Value Can We Expect?

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Data analytics can enable many capabilities that are integral to the modern enterprise: 

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• De-risk data initiatives: It can help to find the nuances & suitability of data, so can de-risk any use case for that data (rather than getting to the end of a big project and realising some vital nuance was missed which undermines the output/value). 
  • We’ve worked with a gas distribution network to assist with regulatory reporting for surveys & fault resolution in multiple occupancy building: we realised that most of the survey data related to buildings found to be outside of scope, so we were able to (a) only report relevant data (b) focus our further data discovery efforts on only the relevant data.
• Valuable insights: Data analytics can directly help businesses improve performance (cost reduction/efficiency, revenue opportunities, managing risk) using tried and tested techniques & visualisations:
  • We’ve helped one of the world’s most highly regulated financial services organisation understand the scale of missing data in their technology obsolescence model and how that was skewing results.
• Drive impact: Data analysts can build a narrative around insights in the context of the real world, helping to drive action from insights (not just pretty charts to amuse!)
  • For a global financial services enterprise, we were able to build a simple scenario model to visualise the scale and nature of the problem, which we presented back and got the buy-in to introduce a way to consider missing data in the model as well as focus on capturing the missing data..
• Measures success effectively: By finding and presenting the right measures of success we can help businesses identify when things are going wrong, and help them steer their teams towards common goals.
  • Working with a multi-national power network operator to find a better way to track plan churn. The current approach was actionable and used fixed time horizons. As we were already helping them move to a more continuous rolling plan we needed a much faster feedback loop and established a new way of tracking the avoidable churn on a daily basis.

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Data Analytics and the Impact on Data Literacy

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Data analysts often get asked for specific numbers or charts. Part of their job is to ask why, until they understand what the person really wants, then they can work together on a solution (which may or may not involve data analytics).

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A good data analyst will educate others on how to analyse data themselves and avoid bias where appropriate, and when to ask for help. It’s a delicate balance but if you want to embed data-informed decision making into your organisation then it will only work with self-service for simple requests, otherwise your data analysts will drown in requests.

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Who Uses Data Analytics?

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Almost anyone is going to benefit from insights into relevant data: it can help them identify opportunities, improve efficiency and mitigate risks.

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Data scientists often spend a significant amount of time on data “cleansing”, having to perform EDA, before they get to leverage their skills in advanced statistics or ML.

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Data engineers are often expected to design & build data models and even create dashboards, but often they lack business analysis experience and/or they’re not empowered to spend time really getting to grips with the business process which the data relates to.

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For data analysts, this combination of business analysis and data exploration are their bread & butter: while they may lack advanced engineering & data science expertise they are honed to pick apart data & find its nuances, and to tell a story with it which helps the business.

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What Ethical Considerations are Important in Data Analytics?

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We have to be especially careful with data which could be used to identify a person, for both legal & ethical reasons. This includes pseudonymised data. Are we using this data in a way which the person would expect? What is our justification for analysing it?

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Statistical bias: There are many traps to fall into when analysing data. We can’t always trust our intuition, as demonstrated by famous examples of survivorship bias & Simpson’s paradox.

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Cherry picking: People can find a statistic to justify almost any opinion they already hold by cherry-picking results. Data literacy is an important tool to ensure we draw appropriate conclusions from data and don’t misrepresent it.

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How Mesh-AI is Using Data Analytics to Think Differently

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We’re implementing this approach at multiple enterprises across the financial services and energy industries. By harnessing data analytics effectively, we’re building capabilities for organisations who want to become more data driven. 

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Find out how Mesh-AI mitigating millions in penalties for this global finance powerhouse

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