Data analytics — an assortment of applications from primary business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics — is increasingly used in industries to enable organisations to make more informed business decisions. It helps 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 is stimulating business performance, however, a recent survey by a US data analytics company Incorta found 30 per cent of business leaders in the MENA region said that they do not still have the basic data analytics infrastructure in their enterprises. This indicates the low satisfaction of the business leaders about the current state of the data analytics infrastructure.
According to the survey representing education, healthcare, retail, banking, manufacturing, and telecommunications sectors, around 42 per cent of the participants said their companies have no BI tools because these tools are not widely adopted in the region, and the majority of the surveyed companies reported that they use between 40 per cent to 80 per cent of their data. Globally, most companies are yet to come up with a mature plan for operationalising analytics.
According to SAS research, less than half of the best models get deployed, while 90 per cent of models take more than three months to install. In the study, 44 per cent of models take even more than seven months before they reach production.
In the MENA region, about 66 per cent of the Incorta surveyed leaders stressed their plans to invest more in data analytics this year, a matter which showcases a massive appetite for investing in this sector.
According to experts, the top data analytics trends are automation of query processing and analysis, self-services, and machine learning, which will push investments in data analytics forward in the next one year, such as improving the decision-making process, improving the employees’ productivity, changing the way of organising operations, and reducing time to market new products and services.
Here are a few ways to increase the effectiveness of data analytics in a business:
Secure ecosystems
Analytics can be operationalised into security practices to help organisations be more proactive about reducing cyber risks. With analytics, organisations can easily identify and deter threat actors like the latest ransomware attacks faster than traditional manual processes. Analytics have also been instrumental in helping IT organisations create secure ecosystems to support a remote workforce. For instance, at APS Marketing, analytics are being operationalised 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.
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Better decision-making
When big data joins forces with AI, ML, and data mining, companies are better equipped to make accurate predictions. Companies can prioritise analytics to tailor products and services and enhance the customer experience — understand the customer journey across multiple platforms while improving retention rates and repeat purchases for retail e-commerce transactions. Data analytics automates what was once a year-long accumulation of knowledge, providing faster and accurate information.
Improve productivity
Analytics has proven to be a valuable asset in helping the firm streamline operations and boost timelines for conceptualising and delivering innovative products. The position shifted as analytics capabilities evolved, and operationalisation 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, maintaining a social presence, and optimising resource allocation.
More accessible analytics
AI is helping to get data analytics in the hands of more people in an organisation by democratising the process of generating reports and making sense of the findings. With self-service, intelligent tools in place, organisations can gain complete visibility into their operations across all departments.
Automation
A report by EY estimates that automated data processing can support around 65 per cent of HR tasks, including payroll processing, candidate screening, and data cleansing. According to McKinsey, businesses can automate 69 per cent of time spent on data processing, which stands to increase business effectiveness while reducing costs–data processing tasks include everything from processing loan applications, and customer support queries to manually processing invoices and forms.
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Predictive modelling
It allows organisations to understand the root causes behind problems and predict future outcomes. Financial services use predictive analytics tools to identify fraud risks and determine creditworthiness. Additionally, there are tools being used to support policies around climate change and conservation, nuclear power, oil drilling, and more. Also, data analytics, combined with statistical algorithms and historical data gives marketers the ability to predict consumer behaviours and outcomes more accurately, then apply key insights to future strategies.
Optimised messaging and solutions
Companies can use data analytics to identify what customers want/what messages they respond to and apply those insights in marketing/development/sales strategies.
Accurate measure of campaign ROI
Advanced analytics tools help measure the impact of all campaigns that contributed to converting a customer. These insights give organisations a framework for future strategies – what channels, actions, and content were most (or least) effective – and help companies make better budgeting decisions.
Increases Revenue
Analytics plays a bigger role in decreasing business costs and increasing revenue. According to a study, companies that adopt data-driven marketing strategies can increase revenue by 20 per cent and reduce cost by 30 per cent. Analytics is most useful to monitor e-commerce activities, ad campaigns, and multi-funnel channels. It will help you measure their performance and effectiveness, making it easy to see what works and what doesn’t.
