Data analytics for competitive advantage

By Takunda Chingonzo | May. 24, 2024

Through data analytics, companies can identify market gaps, analyse consumer feedback and track industry trends.

DATA analytics, in the most simplest of terms, is the art and science of extracting meaning from data.

It is akin to having an ear on every aspect of the business, constantly listening to what the business needs and adjusting the speed, direction and focus of the business.

It is the ability to see trends, pattens and hidden connections beyond the superficial or vanity metrics. What is data in this context? It is information created from every business action and activity.

From customer purchases and production line metrics to website traffic and employee engagement. Whether you are aware of it or not, your business is constantly generating data.

The big question is whether that data is being consciously collected and stored. Data analytics is the process that helps your decipher the story that your data is telling.

Data analytics starts with knowing what data to collect and measure. It is important to know what data is relevant to the business and what is simply just noise. Knowing what to measure is determined by the fundamental and operational needs.

In other words, what aspects of the business do we need to optimise, to improve on or to report on.

Peter Drucker put it succinctly in saying that “we can’t improve what we don’t measure” and this measurement and collection of data has to be infused into the culture of the organisation.

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It has to be baked into the systems and processes that set the cadence of business operations. When we measure something like revenue growth or units produced over time, we learn how the business is performing: business growth against the business plan, improvement over time and comparison against the industry standard.

This process is what data analytics supercharges, by generating insights in real time about the business operations.

So, presuming that (a) our strategy has informed us of what we need to measure, and (b) we have planned and implemented data collection systems and processes.

The next and most critical stage is to ensure that (c) insights can be extracted from the data. It means that the data must be available and kept in a way that allows for manipulation.

Simply having manufacturing records in filing cabinets slows the entire process. Data analytics for business refers to tools that convert raw data into relevant information that can help decision makers and strategic operators in the organisation. So it is critical that our data is useable, accessible and transformable!

This also speaks to the business having within its structures, (d) personnel and resources that are wholly dedicated to this process of converting data into insights, and communicating these insights across the organisation.

In many cases, organisations have “vanity analytics”: Metrics and KPIs (key performance indicators) that are defined by human resources that act as superficial quantitative indicators that do very little in the way of proffering real-time actionable insights.

Just as the new Zimbabwean Cyber Security and Data Protection Act requires “data processing” organisations to have a data protection officer, so must the business have a team or department that harvests, processes and communicates insights drawn from the data that this new legislation seeks to protect.

Data analytics can provide a significant competitive advantage. It improves decision-making accuracy, provides insights into market dynamics, increases operational efficiency and stimulates realistic innovation, ultimately positioning the organisation ahead of competitors and the wave of ever-changing economic forces that put pressure on the business.

Remember that data analytics can generate insights from data that is not related to the business. I am talking about exploiting critical data sets like historical market prices and market performance, for example.

The most crucial super power that data analytics imbues on a business is up to date insights that inform decision-making. By analysing historical data and current trends, organisations gain insights into market dynamics, customer behaviour and competitive landscapes.

This enables proactive decision-making, risk mitigation and the formulation of strategies aligned with market demands. The insights derived from this key activity empowers the business to streamline workflows, allocate resources more effectively and reduce operational costs.

Once a business has a robust data analytics team that is utilising both proprietary internal data as well as external data to deliver exact insights to the leadership, the business starts unlocking the next level of efficiency and that is predictive analytics.

This is akin to the ability to predict the future in the near term with a high statistical probability. Yes, I am talking about the closest thing to predicting the future. Predictive analytics involves using statistical algorithms and machine learning to forecast future trends.

Companies can leverage predictive analytics to anticipate market trends, customer preferences and potential risks. This proactive approach allows businesses to stay ahead, adjust strategies and capitalise on emerging opportunities, keeping the business many steps ahead of the competition and thus moving the objective of the data analytics team from cost reduction and management to profit mix optimisation and maximisation.

Think of it your own team of soothsayers, trustworthy fortune tellers and psychics who can reliably predict the future based on how the firm has performed thus far.

Through data analytics, companies can identify market gaps, analyse consumer feedback and track industry trends. This information is invaluable for product development and innovation, allowing companies to introduce solutions that meet evolving customer needs and stay ahead of the competition.

Companies can also use analytics to target specific audience segments and personalise marketing strategies. For instance, analysing customer demographics and behaviours so as to create targeted campaigns.

By understanding the different types of customers within a market segment, the use of analytics can result in the identification of new customer personas with additional spending propensity, that are not being adequately served.

However, building data analytics into an existing business is no simple thing. As mentioned before, it has to be about more than strategy. It is about building a data centric culture.

Simply mandating employees to measure certain aspects of their work will result in them only caring about said measured aspects at the cost of other key business functions.

We must be careful because in the end, we get what we measure. The use of data in business operations also brings with it other concerns such as data security concerns, ensuring data quality, integrating diverse data sources and addressing the shortage of skilled professionals.

Ultimately, the biggest hurdle to adoption is overcoming resistance to change within the organisational culture. At the core of this business transformation is change management.

Companies can address some of these issues by investing in staff training and skill development, establishing strong cyber security measures and implementing data governance frameworks.

Creating a data-driven culture entails encouraging co-operation, developing clear data policies and incorporating data-driven decision-making into organisational processes.

Finally, developing a data-driven culture is more than just a strategic necessity; it is a prerequisite for obtaining and maintaining a competitive advantage in today’s corporate environment.