How Businesses are Using Data Analytics to Improve Operational Efficiency

Operational Efficiency

Markets around the world are getting more competitive, which is requiring businesses to change their strategies and approaches to cater to changing customer needs so they can remain afloat. Modern consumers are smarter than they have ever been as they know what they want. Increased customer knowledge means traditional ads no longer work as well as they used to. Businesses must therefore offer them better value for the amounts they spend, coupled with better experiences. 

Data Analytics is the Answer

Data analytics and the various tools that accompany it have emerged as a crucial element in helping businesses provide customers exactly what they want. In many cases, delivering exactly what customers need requires certain improvements in business operations. 

Improvements in operational efficiency call for reducing costs to give customers more for each dollar spent, automating and digitizing operations and experiences, as well as optimising and diversifying products and services.

Businesses collect a lot of data, and these data must be processed, analysed and then used to provide insights that businesses can use to enhance their operations. Data analytics is at the heart of all these operations.

Measuring Operational Efficiency

Before a business can start looking at how to improve operational efficiency, it must know how to measure it. The two main factors determining operational efficiency are expenses and profits. Lower costs compared to profits means the business is operating at a greater operational efficiency, while the reverse is also true.

The key to improving operational efficiency is optimising business processes such that the expenses are lowered, and profits are increased. Some key metrics that businesses must keep an eye on include customer behavioural patterns, macroeconomic trends, service delivery efficiency, product customisation and more. All the information that businesses require to determine what is going on in all these areas lies in the data they collect, and data analytics can help businesses make sense of it all.

Areas Where Operational Efficiency Is Crucial

Improving operational efficiency in a business requires businesses to focus on various areas of concern. The challenges businesses face when improving operational efficiency lie in a few areas and specific processes. One area where operational efficiency is crucial is process optimisation.

To attain improved operational efficiency, businesses must look into automating and optimising many of their processes. Doing so minimises the time it takes to complete certain processes while also helping reduce the cost of doing so. Automated and optimised business processes have also been shown to help improve employee productivity and output.

Businesses must also maintain high standards of risk management. Poor risk management means a business is open to various threats that can make it difficult to deliver to customers. Even when the business can do so, it will be late, and its products and services will likely be costly.

Businesses must use available big data, artificial intelligence, and machine learning tools to identify possible risks. Some of these risks include malware attacks, fraud, and money laundering.

Customer Demand and Expense Management

Customer demands are always changing, and successful businesses already understand that growing at scale and speed means meeting these demands. Businesses have access to vast amounts of data that can help them predict consumer demands and see when sentiment starts to change. These valuable insights apply to businesses that serve both individual customers and other businesses. 

Failure to use these insights to predict and meet customer demands means a business cannot improve the quality of its products and services, which hinders its ability to attain operational efficiency.

Lastly, businesses must use the data they have to manage expenses. Trying to attain operational efficiency can be costly. Businesses must know how much each of the required aspects will cost and manage these costs. The aim is to achieve maximum efficiency at the minimum cost possible. 

How Data Analytics Is Useful for Businesses

Knowing the areas of operational efficiency is not enough; businesses must also know how to use data analytics to achieve this aim in these areas. One way in which businesses can use data analytics to do this is by using it to identify market needs.

We discussed that businesses need to meet customer demands to keep growing. Businesses collect data from numerous areas including their websites, social media channels, marketing efforts, third-party providers and more. All of this data can be compiled and analysed for market and customer prediction applications.

The insights businesses get from this data analytics exercise can then be used to understand the market and consumer needs. Such data can also be used for precision predictions because different markets and regions will have different needs and businesses cannot serve their different customers and target areas like one homogenous unity.

Once businesses understand individual and region needs, they can tweak their products services and marketing better. They can also know what not to focus on, which will help them reduce expenses and improve operational efficiency.

Another important way businesses can use data analytics to improve operational efficiency is using it to eliminate supply chain issues. For businesses dealing with physical products, supply chain hurdles are one of the biggest sources of operational inefficiencies. 

Resolving Supply Chain and Employee Inefficiencies

Logistics issues usually come from nowhere, and when they do, they impact all parts of a business, from material sourcing, to manufacturing, and supply. Businesses can use data analytics with accompanying tools like machine learning and artificial intelligence to not only predict issues in a supply chain, but to also come up with solutions quickly. 

Businesses can use these tools to plan for redundancy in the supply chain to eliminate delays that lead to time wasted and increased operational costs.

Data analytics also helps identify and resolve team coordination issues. For a business to run as efficiently as possible, everyone must work seamlessly with everyone else. Sometimes, these synchronisation issues exist between departments which cripples unrelated departments, thus leading to operational inefficiencies. A data analytics expert can help resolve such conflicts and synchronisation issues. They can then put forth remedial solutions so that individuals and departments end up working like one cohesive unit. 

Another cause of operational inefficiency is team and individual productivity issues. If an individual or team is not doing their part, the overall productivity and output of the business suffer. For companies with more than a handful of employees, checking whether everyone is being productive is impossible. However, various data analytics solutions can help identify which employees are not being as productive as expected. Once the management identifies why an employee is not hitting their key performance metrics, they can talk to them and deploy corrective measures to rectify the issue.

Sluggishness in one operational area leads to a domino effect where all related departments end up slowing down as well. All business processes rely on certain steps that come before or after these processes. A delay in this workflow should be identified and rectified as soon as possible. Businesses can also use data in their switch to automated processes. Since automation remains one of the most important ways of improving operational efficiency, investing in automation is a great way to achieve operational targets. Businesses cannot do this using outdated IT infrastructure. Both software and hardware issues can hinder the switch to automated processes. Collecting the right type of data and using it to see where these inefficiencies lie is therefore crucial for all businesses.

Keeping Costs Down

Cost overruns can impact workflows and business processes, and this is why it is so important for businesses to keep an eye on their costs. Businesses have to figure out how much is being spent on things like IT infrastructure, internet sanitation, electricity, salaries and much more. Sometimes, hidden costs crop up in various departments and take the keen eye of an accountant to detect. 

Smaller businesses that want to know the causes of cost overruns but cannot afford an accountant can take advantage of data analytics to do so. The right tools can collect, process and tabulate data to provide graphs and charts that show where money is being spent. Once a business has this data, it can find areas to cut expenditure as well as reduce costs to prevent overruns.

Approaches Businesses Can Use to Improve Operational Efficiency

There are various tools and approaches businesses can use to improve their operational efficiencies. The three main ones are big data, machine learning and artificial intelligence. Incorporating these tools and approaches in business operations has become much easier, with specific tools built for businesses to take advantage of one, all three or a combination of the three tools.

Deep learning tools (AI and machine learning) are a crucial asset in the development of useful practices in operational and process automation. They can be used to identify issues that come up, help keep an eye on areas where a business wants to improve and help with the creation of automated and optimised business operations. 

Because these tools are widely available, they can help businesses start seeing improvements in their processes quickly and with minimal investment.

Predictive Analytics

These tools can help with predictive analysis. Predictive analytics uses big data tools and historical data to make predictions about future outcomes. Predictive analytics is an important tool in understanding both market changes and consumer behaviour. By doing so, it can help a business understand which areas to focus on to take advantage of changes in the market. By understanding what consumers are likely to want in the future, predictive analytics can also help businesses eliminate areas of redundancies and processes they no longer need, both of which help improve operational efficiency.

Predictive analytics can also be used to resolve many of the supply chain issues businesses face. Eliminating these issues improves material sourcing, manufacturing, and shipping, all of which affect both price, customer experience and how customers view the business.

Real-time Analytics

Real-time analytics tools help businesses do all the above in real-time. Doing so reduces the amount of time between the detection of an issue in business processes, solution implementation and the application of these solutions. An important area where real-time analytics come in handy is risk management. Businesses must keep an eye on their systems to detect threats as soon as they crop up. Additionally, real-time analytics tools can help businesses remain compliant with set regulatory requirements.

Big Data Analytics

Big data analytics is crucial for customer analysis. To provide the best product and services as well as learn the areas to focus on to do so, businesses need data on how customers feel about the business, its products and services. Big data analytics can provide all this information.

Additionally, big data analytics can help businesses assess the pros and cons of their existing operations and service delivery. Having this information allows businesses to improve their products, enhance user-friendliness and speed up the process of meeting the demands of existing and new customers.

To improve operational efficiency, businesses require candidates who can use data analytics and the tools discussed above to produce insights that help them identify the source of these inefficiencies as well as areas of improvement. Because of this, there is high demand for individuals with the right data analytics tools. Enrolling in the online master of computer science in data science program at the Worcester Polytechnic Institute will arm you with the skills employers are looking for and help you thrive in data analytics.

It is safe to say that data analytics, machine learning and artificial intelligence can improve all areas of business efficiency improvement, both at the individual and organisational levels.

Conclusion

Operational inefficiencies are one of the main reasons businesses fail to meet their goals, improve their products, and meet the demands of their existing customers. Data analytics can help businesses know where these inefficiencies lie and be an important tool in helping put measures in place to eliminate them. There are numerous approaches businesses could use, but the use of tools like big data analytics, machine learning and artificial intelligence wielded by the right data analyst is the best approach for all types of businesses.

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