By Clarity Insights,
Clarity Insights is a strategic partner to the nation's leading data-driven brands.

What type of flowers should you get for your mother on her birthday? Let’s say she’s 60 years old and comes from Maine. She might love an arrangement that featured Lupines and Yellow Violets. Those are native to Maine.

In addition to pleasing your mother, you’ve also just demonstrated the principles of advanced analytics. You instantly processed numerous variables about your mother’s preferences and came up with the perfect floral choices. But, what if your job was to recommend birthday flowers for 50,000,000 American women? That’s where things get interesting, and a bit challenging.


Advanced Analytics Vs. Business Intelligence

The term “advanced analytics” suffers from hype and overuse in the worlds of business and IT. It has become a buzzword, taped onto an assortment of products that don’t fit into the category. Therefore, you can be forgiven for getting a little confused about the difference between simple data analysis tools such as Business Intelligence (BI) versus far more sophisticated data analytics solutions. Let’s take a quick look at what’s what.

BI involves analyzing structured data, e.g. tables from spreadsheets and columns from databases, to make assessments of trends and performance. For example, with a BI tool, you can extract sales data from the last five years and compare sales year over year. You might see, for example, that sales are not growing overall, but in regions like the South, you’re growing in double digits. In the Western states, though, your business is shrinking badly. BI can tell you what’s going on, but in general, it can’t tell you why. That’s where advanced analytics come in.

Contrasting with the relatively simple capabilities of BI, advanced analytics comprises autonomous or semi-autonomous examination of many different types of data to discover deep insights or even make predictions about the future. It’s the science of using logic. Modes of advanced analytics include actions like forecasting, graph analysis, simulation, network and cluster analysis, multivariate statistics, event-processing and beyond.

In the flower example, an advanced analytics platform might examine your inventory of flowers and compare it to likely orders based on in-depth customer profiles. Based on millions of “my mother is from Maine so she might like Lupines” type logical statements, advanced analytics could warn you that you’ll run out of Lupines and miss the opportunity to hit maximum floral sales.

We’ve kept this example simple, but in reality, advanced analytics employs highly sophisticated tools and data science techniques. To reach its conclusions, an advanced analytics platform might do data mining or text mining across both structured and unstructured data—things like social media comments, documents and so forth. It can do pattern matching, data visualization, sentiment analysis and on and on. Regarding birthday flowers, an advanced analytics tool might discover a sentiment hidden in millions of tweets and Instagram post that red roses are out of favor this year. It could then recommend staying away from promoting roses.

Examples of Advanced Analytics

Businesses and government agencies are putting advanced analytics to work in a fascinating variety of cases. Advertising and marketing provide one of the best and most relatable examples. When you browse Facebook, for instance, you will see ads targeted to you. They’re not just aiming at your demographic, like a radio ad that figures you’re middle aged if you’re listening to an oldies station. No, Facebook’s deeply sophisticated analytics algorithms can process a huge array of data about who you are—based on what you look at, your posts, your comments, your location, your friends—and offer you an advertisement that is usually dead-on accurate as to what you are interested in at that moment.

The security field offers another example. Catching hackers often relies on finding anomalous behavior on computer networks. The trouble with this approach is that no human being can ever study network logs quickly or deeply enough to see the hacker in action. A machine can, however. Cybersecurity systems use advanced analytics to spot network activity that suggests hacking is taking place.

For example, when a hacker steals data in a breach, he or she must exfiltrate it. If you’re running the Security Operations (SecOps) team, how will you differentiate between data that your company is legitimately sending over of network and stolen data? A security tool that uses advanced analytics can train itself to notice oddities, like an “employee” who always sends data to Uzbekistan at 2:00AM, when everyone is away from the office. By flagging this employee, who is probably a bot created by the hacker, the advanced analytics tool can alert SecOps to investigate.

The Business Potential in Advanced Analytics

Advanced analytics has the potential to drive better business outcomes. Results of the process include success factors like better customer engagement and strengthened customer loyalty. These increase earnings by reducing marketing costs required to replace defecting customers. As the flower case illustrates, advanced analytics could also help cut spending on inventory that will never be sold. This also boosts earnings. Strong security keeps the likelihood of a bad data breach down, which is good for the brand and avoids the very costly experience of remediating a breach.

Implementing Advanced Analytics

Doing data analytics properly, so it will deliver measurable business advantage, is not a push-button process. It takes a high level of commitment and investment to enjoy success. However, it’s a manageable project when you have the right partner and tools.

In our experience working with numerous clients on advanced analytics initiatives, the best practice is to establish a modern data architecture as a first step in the process. A modern data architecture comprises a cloud data platform, customized extract-transform-load (ETL) functions, a data integration layer, data security, metadata management, data governance, a data ingestion framework, data lakes, and more. By embracing these elements of the data architecture with a knowledgeable partner, you can realize the business benefits of advanced analytics.

Learn more about our advanced analytics offerings and modern data architecture programs.

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