Dashboards, cockpits, scorecards, data visualisation, performance reports, insights platforms, data portals and many more solutions to mention, what do these have in common?
They all fall under the sphere of analytics, a word that is often used with numerous contexts, yet do we truly understand the role of business analytics?
This starts with analytical thinking and the ability to understand and address complex business problems, finding the root cause and provide solutions. This is often considered the role of the business analyst. This has been identified as one of the 8 Essential Qualities of Successful Leaders1. Does this mean that analytical thinking is now commonplace in business? Stakeholders and leaders seek reassurance in their decision making and evaluation of strategic choices by reviewing where they have come from to where they are going, often relying on analytics.
This is where business analysis and business analytics get confused, the terms are often used interchangeably. Many believing they are one and the same thing. To the purist this is where the difference of what is being delivered versus what is required is discussed. Business analysis on one hand is often driven by a person, typically a business analyst, whose role is to understand the current situation of the organisation, understand the needs and identify challenges both internally and externally. The role of AI has often been touted as a potential replacement to the traditional business analyst. Where business analysis comes to the forefront is through defining business requirements by embracing effective stakeholder management. This is enabled by persuasive interpersonal skills incorporating active communication and storytelling, resulting in the ability to present ideas, solutions and driving change management. The physical business analysis whilst of the utmost importance often lives or dies based on the other factors described. This is often the pre-cursor to business analytics, which take the analysis to the next level and where AI will come into its own.
Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions2. The words quantitative and data are key here, this facilitates the assessment of what is happening, this is where significant organisational investment in technology has occurred over the past five years.
MicroStrategy’s 2020 Global State of Enterprise Analytics Report, found that the top three benefits enterprises are
gaining from analytics were improved efficiency and productivity (64%), achieving faster, more effective decision making (56%), and driving better financial performance (51%)3. This has seen an increased effort in the development of consumable analytics based on cloud technology including data lakes where data is often unstructured with the proviso that it becomes quicker to deploy to the business hence supporting the faster, more effective decision making. However, this has led us towards is a more disparate data environment with conflicted and contradictory data that has not been managed or mastered with the due care and attention required.
Data ownership and governance is often overlooked which causes the black box effect. The data provided is perceived as useful and the demand for data literacy across the organisation is increasing to ensure objective decision making. By providing analytics to stakeholders the assumption and expectation is that the underlying data is providing a true, accurate representation of any given situation and this has to be a minimum standard. Here lies the challenge for the business analyst, often assuming responsibility and becoming the nominated face of analytics. Data integrity is a key topic but near impossible to validate and connect the sources or use the data for business analysis purposes due to the black box effect.
Yet, the same data is used to feed business analytics which are becoming more AI powered and more readily available to users spanning the four domains as described by Harvard Business School2.
- Descriptive: The interpretation of historical data to identify trends and patterns.
- Diagnostic: The interpretation of historical data to determine why something has happened.
- Predictive: The use of statistics to forecast future outcomes
- Prescriptive: The application of testing and other techniques to determine which outcome will yield the best result in a given scenario.
Over the last decade within pharmaceutical commercial analytics space, we have focussed heavily on descriptive and diagnostic analytics to assess what and why something is happening. Progress is being made towards predictive and prescriptive analytics with a projection towards future state and what-if scenarios.
Analytics will always be limited by the data sets available, as an industry we don’t have big data comparable to industries such FMCG or finance sectors. We have no true sales picture of prescriber decision making, although we do have access to CRM data and an increasing amount of digital data becoming available. This does result in the use of assumptions and hypotheses being made and often comes back to the skill of the business analyst in being able to communicate with authority and objectivity.
We need to move forward where there is harmony between business analysis and business analytics. The catalyst for this will be the business analyst enabled by the power of artificial intelligence. Consistency is key, data needs to be clean, mastered, matched, governed and accessible. The power the analyst brings is in adding insights rather than data cleaning, AI can provide the resource to become the trusted advisor to the analyst and wider business.
Together the business will accelerate speed, efficiency and enhance the confidence in the decision-making power it desires.
Article sources
1. Harvard Business Review (Hbr.org): "8 Essential Qualities of Successful Leaders (hbr.org)"
Accessed October 2024
2. Harvard Business School (Online.hbs.edu): “Business Analytics: What It Is & Why It's Important (hbs.edu)”
Accessed October 2024
3. Forbes (Forbes.com): “The Global State Of Enterprise Analytics, 2020 (forbes.com)”
Accessed October 2024
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