Data analytics has always been something of a black box. But thanks to powerful, cost-effective cloud platforms like Snowflake, and intuitive AI-powered software tools, the means to generate business insight is being democratized to more and more users. It offers line of business users an opportunity to bypass historic IT bottlenecks and start driving business growth in a variety of areas. The intelligence needed to make successful business decisions is locked inside internal and external data. The key is ensuring that data is relevant, timely and high quality.
It's also critical that any analytics work can also be undertaken without adding cyber risk. Because a breach of sensitive data could destroy all the value these projects are helping to create for boardrooms across the globe.
A cloudy revolution
Cloud-based data analytics platforms have a critical advantage over previous iterations. They can scale to huge data sets without impacting performance or becoming prohibitively expensive. Let’s take a look at how these tools are transforming three industries:
Financial institutions are already using data analytics to reduce fraud risk, make business processes more efficient and enhance sales.
Consider the steps usually taken to approve a customer loan. AI-powered analytics could crunch data from third-party sources like credit agencies and enrich it with other data – even social media activity – to make faster and more effective decisions. It’s not just processing loans but also opening new accounts or offboarding customers which could be automated. AI tools can help banks to spot where they are being held back by manual processes and where greater use of intelligent machines would help.
Fraud is another big challenge for banks. In the UK alone, criminals stolen over £600m (€682m/$722m) from the sector in the first half of 2022. Behavioral analytics and other techniques can spot patterns of suspicious activity that human eyes would miss, to stop fraud in its tracks – whether it’s a fraudulent purchase, a credit application or an attempt to log-in to an account.
More than perhaps any other, insurance is an industry built on data. Carriers can leverage cloud-based analytics to tap an increasingly wide range of data – not just from within their industry but third-party sources covering location, environment, government and much more. By applying descriptive, predictive and prescriptive models to this data, insurers are enhancing underwriting and pricing.
According to McKinsey, excellence in these two areas is what most industry leaders have in common.
“Even the leading insurers can see loss ratios improve three to five points, new business premiums increase 10-15%, and retention in profitable segments jump 5-10% percent, thanks to digitized underwriting,” it claims. The consulting giant adds that insurers will increasingly use analytics in a similar way to hedge funds – to identity market opportunities faster than their rivals.
For both bricks and mortar and e-commerce players, there are big gains to be made from incorporating AI-driven insight into business planning. Retailers already collect large amounts of data on their customers at every available touchpoint. Analyzing that with the right tools can help them better understand consumers’ buying journeys and requirements. This in turn can be used to improve targeted advertising and marketing efforts, and even to forecast demand for inventory. It can also be leveraged for cross-selling and upselling opportunities, such as suggestions for products that are usually bought together.
Analytics are also being used to better understand rising and falling demand for products and how pricing can be tweaked to influence these patterns. And they could even be used to profile the best geographical locations to open a new site.
Why security matters
It goes without saying that much of the data being fed into analytics tools is highly sensitive, containing customers’ personal and financial information or perhaps corporate data that would be of interest to a rival company. That’s why organizations must first ensure any new data project is built with security in mind from the start.
Rather than focus efforts on perimeter defenses which could be breached by hackers with access to compromised employee credentials, firms should start by securing their most important asset: the data. Data-centric security for analytics should offer:
- Strong protection for the data, in line with industry standards
- The ability to use the data while it is protected (ie via format-preserving encryption or tokenization)
- Automatic and continuous discovery, classification and protection across the entire corporate environment (including cloud systems)
- Protection throughout the entire data lifecycle and at all times – at rest, in motion and in use
- Simple integration with business apps and data flows