Data analytics to drive performance

Drive organisational performance using data analytics

Data analytics is a diverse field that encompasses several types of analyses. From governmental institutions to sports and even entertainment providers, data analysis techniques have become integrated into the modern world. Significant research is readily available on using data analysis to service and add value to consumers, however, its vital role within the internal operations of an organisation is often overlooked. Data analytics can help optimise performance and guide strategic decisions for any organisation. From the volumes of data generated, data analytical techniques can help transform big data into meaningful insights which will ultimately allow the organisation to achieve its goals.

Data analytics to improve organisational performance

Organisations are continuously involved in improving their workflow processes. Due to the crucial nature of these processes, they are frequently reviewed and monitored. To improve such workflows, each process needs to be evaluated for any deficiencies. Data analytics can be used to identify any inefficiencies, bottlenecks, or redundancies in these processes. For example, the time taken between two events can be analysed to better understand delays or if additional resources are required. Costs and expenses incurred within a process can also be analysed to discover if there is an opportunity to reduce costs without compromising quality. Data analytics can also assist in ensuring there is adequate segregation of duties by analysing user approvals throughout a process. Such analyses will not only improve the current workflows but also ensure the relevant controls are in place to reduce the risk of fraud and error.

Manual procedures within such workflows can also be automated for increased efficiency. For example, weekly or monthly reports can be automated to reduce the time taken in gathering and preparing data. Dashboards can also serve as valuable tools that can provide real-time data to be used for decision-making purposes. Automation not only improves the time taken in performing manual or repetitive tasks but also ensures consistent processing and/or computations which reduces the risk of error or possible circumvention of controls. Through the use of data analytical tools and other related software, data can also become readily available and accessible throughout the organisation reducing the time taken in requesting data from different departments and persons. Enhanced workflows will not only result in improved customer satisfaction and better financial performance but also improves employee wellness. Employees working at the correct capacity without being overwhelmed can increase overall employee productivity and satisfaction.

As customer needs and wants evolve, increased emphasis had been placed on consumer and marketing analytics to gather deeper insights, boost marketing strategies, and improve profitability. With the current economic conditions and intensified competition within industries, data analytics is imperative in monitoring and understanding customer behaviour and expectations as well product performance. For example, popular online streaming platform, Netflix, uses analytical techniques to recommend preferred genres of movies and series to users to ensure continued subscriptions and an enriched customer experience. Access to real-time data and analyses can also identify underperforming products/services or product defects. Data mining techniques can be utilised to gain an understanding of client perceptions from reviews and other sources to determine the success or failure of products and services. This information allows organisations to be proactive to reduce financial loss and/or negative reputational impact. Data models are commonly used to assess risks that the entity might face as well as the probability that such risks will materialise. These models incorporated with predictive analytics can be used to forecast future growth and returns which allow organisations to adjust their strategies accordingly.

Data analytics is not only concerned with the analysis of financial data. Employees are the core of any operation. The success or failure of any business is highly dependent on its employees and therefore performance measurement is a crucial element of any organisation. Employee performance can also be measured and monitored with data analytics; however, employee performance does include qualitative characteristics which might not always be quantified or analysed. Using data analytics, more realistic and effective KPIs can be created instead of using an arbitrary method across the organisation. Timesheets, productivity, targets, and actual versus budget variances can be performed in creating better performance benchmarks for employees, thus improving employee morale and retention. Dashboards are also a perfect tool for helping employees keep track of their performance and work toward their goals, keeping them motivated throughout the year.

While the use of data analytics can drive and improve the performance of an organisation, its implementation may have some hurdles. The first step when introducing data analytics is to promote a data culture amongst users and employees. Data should be seen as more than just information stored for record-keeping purposes. It must be a part of the business philosophy to use its data to describe the business in a way that can enable users to make informed decisions and obtain deeper insights.

A common obstacle in using data analytics, is the expectation and communication gap between users of data analytic reports and traditional data analysts. Successful data-driven organisations invest heavily in training of non-traditional data analysts and data analysis users. These employees are trained to understand and access organisational databases and tables. It has been shown that data analytic contributions increase significantly when consumers understand where the data resides and the data available in the organisation’s data lakes and production databases.

One of the most significant challenges when using data analytics is gathering meaningful and quality data. Excluding the complexities of collecting and storing such data, the sheer volume of data generated by an organisation may be overwhelming. Compounding this problem, is to obtain relevant data that will be useful to analyse. In most cases, the required data may come from multiple sources which will need to be extracted, harmonised and appropriately prepared to ensure the data can be analysed.

Once data is collected, the correct tool must be implemented to effectively analyse this data. In recent years, numerous new tools have been developed, however, not all of these tools cater to different user requirements. Training may also be required owing to the technical nature of these tools. To obtain the optimum results from data analytics, an organisation might need to centralise its data for easy access to all users. Allowing such data to be accessed also exposes this data to vulnerabilities. Strict cyber security protocols need to be applied so that this data will always remain protected and available while maintaining its integrity. By understanding these challenges, organisations can take action to overcome these obstacles and successfully implement data analytics throughout all departments.

As organisations undergo increased digital transformation, new challenges will arise. However, the vast benefits of data analysis are already being realised. In a world progressively becoming more dependent on data, data analytics can help organisations transform raw data into informative, educational insights that drive performance and effective management.

Salim Mohamed

Data Analyst, Johannesburg