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Towards Digital Transformation: 5 Key Roles for Data Analysts

To monitor, measure, and evaluate various metrics, among several other business functions, business enterprises are turning to data analytics tools.


Whether it is to find out how their digital media campaign is doing, monitor what the competition is doing, or measure their overall business performance, these companies rely more on the immense power and incredible insights data analytics tools offer. With the rise of digital technologies, it has become easier to disrupt and transform virtually any business model quickly.



Depending on their size and industry, many companies are aggressively hiring data analysts at all levels - from data scientists (who focus more on modelling data) to business analysts (who assist with structured data) to entry-level data analysts (report building).

With the aggressive need to quickly integrate important technologies with their business processes, it is little wonder that data analysis has become one of the most sought-after skills globally such that 46% of Chief Information Officers have reported an acute shortage of those skills despite the opportunity for high wages (Woodie, cited in Kotorov 2020).


Although the role may differ, all these professionals generally require strong data analysis skills to be able to help the firm integrate IT and IS successful.

Some of the roles a data analyst can play in the IS/IT integration processes, thus contributing to creating more formidable business processes are outlined and discussed briefly below.


1. Improving Business Decision Making Processes



It is up to data analysts to ensure that business leaders receive accurate information about current trends by verifying that all data points are accurate before integrating actionable insights. Their thorough understanding of how both structured data and unstructured data work enables them to do so with ease - whether it involves a spreadsheet containing hundreds of data points or an audio file containing data describing the company's customer service satisfaction levels (Gandomi and Haider, 2015:138).


2. Identifying Business Opportunities


According to Calegari, Delgado, Artus and Borges (2021:2), data analysis bridges systems and processes, which gives an enterprise insight into everything; from the customer base and industry statistics down to purchase patterns - with business possibilities.


In our data-driven world, data analysts directly impact business growth by using data to inform businesses about their activities in various departments like logistics, finance, HR, marketing, sales, and distribution, among others.


3. Identifying Patterns And Predicting Trends to Enhance Business Processes.


Whether it is the company's marketing activities or sales performance, these data (patterns and trends) insights can directly improve business processes for the better.


For example, once the analyst successfully wrangles the unclean data, a task that constitutes as much as 80% of their working time (Dasu, cited in Kandel et al. 2011), to arrive at accurate data that highlights valuable information about their competitors' performance, decision-makers then visualise the results to fetch insights that subsequently guide their next moves, such as implementing an aggressive advertising campaign to win customers from their competitors or a new pricing strategy to boost competitiveness.

4. Collaborating With Other Departments To Improve Data Quality And Build Trust In The Systems




Data analysts sometimes work closely with data scientists and data engineers (or data developers) to build new data components of business intelligence systems such as data warehouses, data marts, and data lakes.

Working together, they help improve the quality of information available to decision-makers by harnessing better data sources and following more rigorous standards for data warehousing, modelling, and analysis.

5. Guiding Non-Technical Employees on Data Science and Big Data Technologies to Improve Business Decisions


As new technologies emerge, such as Artificial Intelligence, Machine Learning, and the Internet Of Things, data analysts must stay informed.

In turn, these professionals can then explain these new developments to managers, C-suite executives, and other colleagues to incorporate relevant data science and data engineering techniques into the entire business process, thus bridging information technology with information systems.


Conclusion


As the competition among business enterprises gets more intense in the 21st century, firms worldwide must continue to evolve, finding new ways to transform digitally and stay ahead of the curve. Consequently, the data analyst role will become even more crucial as AI, ML, AR, VR, and other advanced technologies become integral parts of business processes.


References


Calegari, D., Delgado, A., Artus, A. and Borges, A., 2021. Integration of business process and organizational data for evidence-based business intelligence. CLEI Electronic Journal, [online] 24(2). Available at: <https://doi.org/10.19153/cleiej.24.2.7> [Accessed 24 November 2021].


Gandomi, A. and Haider, M., 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, [online] 35(2), pp.137-144. Available at: <https://doi.org/10.1016/j.ijinfomgt.2014.10.007> [Accessed 22 November 2021].


Kandel, S., Heer, J., Plaisant, C., Kennedy, J., van Ham, F., Riche, N., Weaver, C., Lee, B., Brodbeck, D. and Buono, P., 2011. Research directions in data wrangling: Visualizations and transformations for usable and credible data. Information Visualization, [online] 10(4), pp.271-288. Available at: <https://doi.org/10.1177/1473871611415994> [Accessed 24 November 2021].


Kotorov, R 2020, Data-Driven Business Models for the Digital Economy, Business Expert Press, New York. Available from: ProQuest Ebook Central. Available at: <https://ebookcentral.proquest.com/lib/salford/reader.action?docID=6178484&ppg=66> [Accessed 23 November 2021].


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