Advanced Analytics vs. Business Intelligence
This article is about the differences between BI and Advanced Analytics (and the benefits of the latter over the former).
The different types of advanced analytics (aka augmented analytics) include:
The benefits of advanced analytics include:
- Predictive maintenence
- business monitoring
- Reduced churn
- Better customer experience
According to a West Monroe Partners’ survey, 68% of business and technology leaders surveyed don’t believe their competitors are leveraging data successfully. It’s a surprising statistics in 2019, with all the talk of advances in machine learning and artificial intelligence, and talk in the business community of advanced analytics.
But just what is advanced analytics? Gartner coined the term in 2014 and defined it as “the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.”
Advanced analytics, sometimes also referred to as augmented analytics, relies on such techniques as data and text mining, pattern matching, forecasting, sentiment analysis, network and cluster analysis, multivariate statistics, and others.
All of these techniques, of course, are well outside of what we traditionally think of as business intelligence methods and practices. To be sure, BI is important—crucial even—and some advanced analytics build upon business intelligence data. But in today’s ever-changing, warp-speed world, it simply isn’t going to get the job done because business intelligence is too limited. It uses spreadsheets and only answers such questions as “what happened?” “how many?” and “how often?” Advanced analytics, on the other hand, takes large volumes of structured and unstructured data to tell us “why?” “what if?” and “how?”
The difference between BI and advanced analytics
Business intelligence—largely comprised of spreadsheets, pivot tables, and reports—focuses on reporting and querying; advanced analytics, on the other hand, is made up of structured and unstructured data and is about optimising, correlating, and predicting the next best event or action.
And where traditional BI tools examine historical data to tell us what happened, tools for advanced analytics focus on forecasting future events and behaviors, enabling what-if analyses so that companies can predict the potential impact of a change to its bottom line.
Benefits of advanced analytics
- Business monitoring: Companies can use advanced analytics, specifically to automate anomaly detection, in order to improve operational efficiency and protect revenue at a more granular level. For instance, a retailer may decide to monitor the hour-by-hour purchases of a recently released product to see how sales are doing. Automated anomaly detection can detect - in real time - unusual purchase trends in dimensions as granular as store location, or browser type and application version. Perhaps the store’s manager forgot to put the product out. Or there could be a bug in an app version. Both are anomalies that could easily go unnoticed but collectively impact that product’s revenue.
- Predictive maintenance: For most companies, cost containment is as crucial as profit increases. So it is that maintaining infrastructure is a critical factor for many of them, and predictive maintenance therefore becomes key. It analyzes metrics and data related to lifecycle maintenance of technical equipment, allowing companies to streamline costs and avoid downtime.
- Reduced churn: One company, Paschall Truck Lines, recently used advanced analytics to its advantage. The company moved its database, which contained records on everything from the driver’s past experience and work history to geo-location and more, to advanced analytics platforms. They have been identifying correlations between hiring areas and retention terms, which has led to lower employee turnover.
- Better customer experience: Advanced analytics will prompt improved customer engagement when companies use it to monitor social media and review sentiment. Companies can discern correlations between Twitter mentions and sales data, thereby allowing marketing teams to adjust campaigns in real time.
- Next best action: Market segmentation is an important tool of advanced analytics. But that’s only part of the story, because advanced analytics can also offer up the best way to approach each of those segments. Through the analysis of everything from buying patterns to consumer behavior to social media interactions, you can develop a holistic view of customers, thus garnering a better way to connect with each of them as individuals.
Types of Advanced Analytics
1. Diagnostic Analytics
Diagnostic analytics is the closest cousin to BI in that it focuses on the past, but that is where the similarities end. It doesn’t come back with a clear-cut answer, but instead provides a probability, likelihood, or distributed outcome that must be interpreted by a data scientist or other knowledgeable expert.
Diagnostic analytics answers the question: why did it happen? It includes techniques such as drill-down, data discovery, data mining, and correlations. Diagnostic analytics can help to identify outliers and patterns, and it can uncover relationships.
2. Predictive Analytics
Predictive analytics takes the what happened of BI and why it happened of diagnostic analytics a step further by applying machine learning algorithms, classification models, and regression models to historical data to forecast potential future outcomes.
Predictive analytics answers the questions: what if? Like diagnostic analytics, it generally requires the expertise of a data scientist, or other data analytics specialist. Predictive analytics mines data from business databases, such as CRM, ERP, marketing automation stacks, and others, and it’s proven itself as an important tool for learning about customer interests, uncovering new business opportunities, lowering costs and reducing risk, and especially, eliminating problems before they occur.
3. Prescriptive Analytics
Prescriptive analytics builds upon predictive analytics by helping an organisation to match a recommended set of actions to a desired potential outcome. Prescriptive analytics answers the question: how do we make it happen?
It is characterised by such techniques as graph analysis, simulation, complex event processing neural networks, recommendation engines, heuristics, and machine learning. It’s techniques continually “learn” through feedback mechanisms that analyse action/event relationships and then recommend the optimal solution.
Of all the analytics methods, prescriptive analytics has the highest barrier to entry. As such, it is not used by a lot of companies. However, despite its high complexity—or perhaps because of it—it also has the greatest potential value to businesses.
The benefits of advanced analytics are many. For the CTO and CFO, there are clear bottom-line benefits. There is also a benefit in terms of business planning, as predictive analytics makes forecasting easier. Then there are the benefits for the technical user in systems that are easier to monitor, with a faster time to deploy and with more real-time analysis. These users are also no longer running routine and basic reports, but are solving more complex queries with advanced artificial intelligence and machine learning.