How data science can revolutionise businesses in Zimbabwe

 

TINAYE MAKONI

I had the honour of speaking at a data science meeting at Harare Institute of Technology towards the end of last year.

I discussed how Claxon Actuaries is using data science to address issues in the financial services sector, particularly in the insurance industry.

Following my presentation, a brief interview with Business Times sparked considerable interest, prompting me to go step further into explaining how data science can revolutionize businesses in Zimbabwe.

I will provide a few examples to illustrate how businesses can easily tap into the benefits of data science. The examples are not an exhaustive outline but represent low-hanging fruit that businesses can easily grab to harness the power of data science.

In essence, data science is a scientific process that involves collecting, analyzing, and uncovering patterns in data to gain insights that shape business decisions.

It empowers companies to understand past events (descriptive analysis), reasons behind them (diagnostic analysis), predict future occurrences (predictive analysis), and determine actionable steps (prescriptive analysis).

Data science leverages machine learning, where programs learn from data and experiences without explicit programming.

Data science enables businesses to make informed decisions based on facts, providing a competitive edge for those who embrace it.

In Zimbabwe, where most of the economically active population, aged 18 to 50, spends significant time on the internet, data science can be a game-changer. Companies can instantly gauge the market’s response to their products or services through conducting sentiment analysis on social media comments. This valuable feedback helps to identify shortcomings. This allows businesses to tailor their offerings to meet customer needs, giving them a competitive advantage.

Data science aids in refining marketing strategies, reducing waste, and targeting specific customer segments. By analyzing customer data, businesses can gain profound insights into consumer behavior, leading to the development of products that precisely address customer needs.

Efficient budgeting is another area where data science can have a huge impact in addressing operational expenses (OPEX) and capital expenditures (CAPEX).

Predictive modeling plays a central role in enhancing forecasting accuracy. This enables businesses to allocate resources effectively to ensure smooth operations and achieving strategic goals.

Under operational expenses, data science empowers businesses to analyze historical expenditure patterns, identify cost drivers, and forecast future operational costs with precision. This allows companies to streamline their day-to-day spending, allocating resources where they are most needed and optimizing overall operational efficiency.

On the capital expenditure front, predictive modeling helps businesses forecast future capital investments required for the acquisition of assets, technology, or infrastructure. This proactive approach ensures that companies have sufficient funds allocated for strategic initiatives. This prevents unexpected financial strain and facilitates the smooth execution of planned projects.

Data science streamlines processes, identifies bottlenecks, enhances overall efficiency and contributes to more accurate forecasts of future product and service demand. By leveraging advanced analytics and machine learning, companies can evaluate and optimize business processes, leading to significant cost reductions. This data-driven approach empowers organizations to make informed decisions about resource allocation. This allows for strategic planning of stocks and personnel. With more precise forecasting, businesses can avoid the risks of both over and under provisioning of resources. Over-provisioning ties up significant capital in excess stocks, while under-provisioning can lead to poor customer or client service. The integration of data science maximizes efficiency and ensures businesses are well-prepared for fluctuations in demand. This contributes to a more agile and responsive operational strategy.

One of the paramount applications of data science is in risk management. By analyzing data to build predictive models, companies can identify potential risks before they materialize. This proactive approach safeguards the business as a going concern. It also allows for the identification of economic risks in volatile environments, such as Zimbabwe.

In volatile environments, such as Zimbabwe, economic risks are inherently challenging. Data science allows for a comprehensive assessment of economic variables, market trends, and external factors. By analyzing this information, companies can anticipate and navigate economic fluctuations, making informed decisions to safeguard their financial stability and strategic objectives.

Data Science can be used in all industries. In insurance, it can aid in accurately pricing products based on distinct risk attributes. Banks can leverage machine learning for credit rating, predicting default probabilities, and calculating expected credit loss. Some of the banks and lending institutions in Zimbabwe are starting to implement this.

In the telecommunications industry, data science can play a crucial role in predicting and mitigating customer churn. In the energy sector, data science can facilitate predictive maintenance for critical infrastructure. By analyzing sensor data from machinery and equipment, energy companies can predict when maintenance is required.

This minimizes downtime and reduces costs. Retailers can capitalize on data science for personalized marketing. Through analyzing customer preferences, purchase histories, and online behavior, retailers can deliver targeted promotions and recommendations, to enhance the overall shopping experience and improving customer loyalty.

In non-governmental organizations, data science can be used to effectively and efficiently allocate resources and manage stocks. Medical aids also benefit from data science to set reserves to meet future claims. Logistics companies can use data science to optimize routes to save costs of fuel and time.

To effectively implement data science, I urge companies to establish dedicated departments or engage with consultancy firms if sustaining a department is challenging. The key is to avoid merely accumulating vast amounts of data without extracting value from it.

Data science is not just a tool. It is the new gold, and those who efficiently mine and leverage it will undoubtedly lead the charge in transforming businesses in Zimbabwe.

Tinaye Makoni is a distinguished senior data scientist  at Claxon Actuaries. He holds  a first class degree in Actuarial Science from  the National University  of Science and Technology. He also holds three AWS certificates (cloud technology). Tinaye’s impactful contributions include the development of key applications such as IFRS 9(Expected credit loss), IFRS 17 PAA and GMM.

He can be contacted for feedback on [email protected]; www.linkedin.com/in/tinayem; or +263771121161

 

 

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