Increasing Effectiveness of Data Analytics in a Business

Data analytics is the process of examining data sets to find trends and draw conclusions about the information they contain. Data analytics is increasingly used with the aid of specialised systems and software. Its technologies and techniques are broadly used in commercial industries to enable organisations to make more-informed business decisions. Scientists and researchers also use it to verify or disprove scientific models, theories and hypothesis.

Data analytics predominantly refers to an assortment of applications from primary business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. It's similar in nature to business analytics, another umbrella term for approaches to analysing data. The difference is that the latter is oriented to business uses, while data analytics has a wider focus.

Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimise marketing campaigns and customer service efforts. It can also be used to respond quickly to market trends and gain a competitive edge over rivals. The ultimate goal of data analytics, however, is stimulating business performance. Depending on a specific application, the data that's analysed can consist of either historical records or new information that has been processed for real-time analytics. Additionally, it can come from a mix of internal systems and external data sources.

Wherein companies have high expectations about the role of analytics, there is still significant work ahead to operationalize analytics modes and maximise the benefit of data-driven decision making.

Companies aim to flex their analytics muscle on a variety of challenges like improving customer experience and engagement, optimising enterprise productivity, and building more innovative products. Companies yet need to address analytics as a holistic operational strategy to reap sustainable business benefits with bottom-line impact.

Most companies have yet to come up with a mature plan for operationalizing analytics. Many firms are allocating significant dollars and sizeable resources to building analytics models that never deliver on their expected value that only serves to waste time and money. As per SAS research, less than half of the best models get deployed while 90% of models take more than three months to install. In the study, 44% of models take even more than seven months before they reach production.

Here are a few ways to maximize effectiveness of data analytics in a business:

Elevating Customer Experience

Many companies are prioritising analytics to tailor products and services and to enhance the customer experience. Some firms are embracing analytics to understand the customer journey across multiple platforms while improving retention rates and repeat purchases for retail e-commerce transactions.

Mixed with playbooks for remediation, analytics can be operationalized into security practices to help organisations be more proactive about reducing cyber risks. With analytics, organisations can easily identify and deter adversarial threat actors like the latest ransomware attacks faster than traditional manual processes.

Analytics have also been instrumental in helping IT organizations create secure ecosystems to support a remote workforce as companies recalibrated almost overnight for the COVID-19 era. For instance, at APS Marketing, analytics are being operationalized into the enterprise security framework to detect phishing exploits, unified communication fraud, and other threat vectors as the vast majority of staffers now work from home.

Challenges Lie Ahead

Wherein companies have clarity about their business goals for data-driven insights, they are not so trained in what it takes to operationalize analytics to make sure they can achieve their goals. What data to gather and use, nor are they aware of potential biases in the data. Additionally, data governance is an area that many companies struggle to formalize effectively, specifically, how to cleanse and normalize data, so it is of the highest quality and adheres to data privacy and security standards. At the same time, companies can't and shouldn't write a blank check for analytics. Instead, they need to weigh the cost of operations versus the value analytics can provide to an organisation.

In some companies, analytics have proven to be a valuable asset in helping the firm streamline operations and in turn, boost timelines for conceptualising and delivering innovative products.

The position shifted as analytics capabilities evolved, and operationalization strategies allow insights to drive business processes and change. There's so much more to using analytics to build and grow a business, understanding data storage measures, maintain a social presence, and optimize resource allocation. Thus, analytics can improve customer engagement and productivity.