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Data Analytics for Your Business Featured

Data Analytics for Your Business Adeolu Eletu

Data analytics has proven to be a critical part of modern businesses. It can help improve decision-making outcomes for almost any type of decision, which can be micro, macro, strategic, operational, cyclical or tactical decisions that will help propel a company to the best possible heights. Data analytics can help unearth new solutions to problems and opportunities that business leaders would not have found if they continued using conventional methods. Progressive organizations employ data for various things and therefore must rely on data from within and outside the organization to make smart and informed decisions.

How companies use data analytics

Data analytics is a catalyst for digital strategy and transformation. It enables faster, accurate and relevant decisions in the complex and delicate business environment. As such, businesses use it for various activities. When employed properly, it can help address many business issues and offer invaluable insights into business and customer behaviour. Further, data analytics can help your business understand everything starting from how products can be managed and running of PR campaigns to how to mitigate risk, enhance security and increase brand awareness. Below are some ways that big data analytics can be used.

  1. Informed decision-making

One of the leading benefits of data analytics is using data to justify business decisions as stated above. The process involves two phases, the first being predictive analytics to help suggest what can happen in future based on the gathered data. The second one is prescriptive analytics which can be used to indicate how a business should react to the forecasted changes. For example, businesses can determine offerings and pricing based on customer data to raise sales.

  1. Improved efficiency

Most businesses employ big data analytics in streamlining operations. They use it to collect and analyze data relating to business activities like supply chain by identifying where delays and bottlenecks originate from and predicting where the problems might originate from in the future and possibly propose approaches to dealing with them. This enhances operational efficiency and the elimination of bottlenecks. Furthermore, through timely identification of problems, it is easy to address the challenges in time.

  1. Mitigating risk

Like the way analytics can be used to identify and predict business inefficiencies, it can also be used to highlight possible risks and devise preventative measures. Retailers for example can use big data and statistical models to determine the stores that are at the risk of being targeted by shoplifters and enhance security in the stores that are found to be at a higher risk. Data can also help mitigate financial risks in addition to physical risks. It can help limit losses by aiding do things such as determining sell clearance of items and reducing inventory.

  1. Improve security

In the age of social media and the internet, businesses face countless data security threats. However, with the advancement of big data analytics, businesses can enhance their security posture by predicting possible sources of compromise and preventing them from getting into the business and causing reputational damage.

Businesses can use data analytics to analyze and visualize a company’s audit logs to find out the origins of past cyber-attacks. The data can then be used to identify the vulnerabilities within the cybersecurity processes and suggest possible fixes.

  1. Predictive analytics

Predictive analytics is used to deal with probabilities and predict a series of outcomes over time. It can also be used to highlight uncertainties related to various outcomes. Predictive analytics tells us what we should expect and addresses questions about what is likely to happen. This analytics relies on techniques like predictive modelling, regression analysis, pattern matching and machine learning.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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