Business Analysis & Data Literacy
In a recent article published by Forbes, one of most sought-after skills in the coming year across industries will be Data Literacy.
This comes not as a surprise as organisations, over the last decade have become increasingly aware of the importance of data in business. Data is powerful and with the advent of a number of data analysis and visualisation tools and techniques, the possibilities for an organisation who uses this data is limitless.
A Business analyst, in this midst, can avail of these massive opportunities by becoming data literate. Data literacy begins by understanding the pockets of data available within an organisation, collating that data, converting into insights using data storytelling toolsets and sharing these insights with business during the gap analysis and requirements management lifecycle within a project.
An example in point would be the analysis of a required data point, say, customers’ date of birth, for reporting purposes. An analysis into a source system might highlight that whilst data is being collected, it is not consistent, correct or accurate. This, in turn, would highlight.
- Process/training gaps where staff might not be collecting this information,
- Policy and procedure gaps where the requirement to collect this critical information is not formalised or
- System gaps where the date of birth is simply not a mandatory field.
Thus, an analysis of a single data point and the ability to translate it to business–relevant information will ultimately lead to an increased maturity in a company’s processes, people and technologies.
Whilst traditional business analysis and data analysis have been treated as two different capabilities, the line between the two are becoming increasingly blurred. As organisations focus more and more on data driven projects, both regulatory and digital, the need for a business analyst to remain competitive is to develop skills related to data. Data Literacy consists of three high-level facets:
1. Understanding data and its relation to business outcomes
A BA performing data analysis must always focus on the following data dimensions
A flaw in any one dimension above might have big repercussions to business outcomes and hence, need to be analysed, documented and communicated as appropriate. A traditional BA has the core skills for analysis and if extended to analysing impacts of data in a business, this can prove valuable to an organisation.
2. Data Visualisation tools and techniques that lets one “interact” with the data
Current popular tools are PowerBI, Tableau etc. A business analyst with the ability to use these tools can derive great insights that will be useful during the analysis phase and beyond.
A point to note here is that the use of tools might be defined as business analytics.
SQL language for backend data analysis is also useful and a skill most BAs already possess.
3. Breaking down data complexities to business understandable language
Data is complex and a business user might not be able to fully grasp the implications of what the data is telling him and what it represents.
A BA is already skilled at managing stakeholder communication and hence can use these existing skills in ensuring that the data complexities are broken down into what a user can understand so that the message is fully understood and actioned.
A business analyst who is already adept at process & requirements analysis can play a big role in enabling change and contributing to business growth in organisations, by acquiring data related knowledge and skills. He/she will be able to convert raw data into meaningful business information, which in turn will allow a business to act upon areas of low ROI, focus on functions that need improvement and use insights provided by data to grow itself.
At the same time, organisations must commit to spending the effort, time and often, dollars in bringing in a shift in focus from traditional analysis to data analysis, upskilling BAs and really investing in enterprise information management for building a more robust data framework.