For a competitive edge in 2018, every business, from start-ups to multi-national behemoths must recognize the invaluable contributions that good, clean data and the strategic implementation of business intelligence tools can make, helping empower all employees to make better decisions.
I've been looking at the top ten lists of some of the best-known data authorities and have compiled what I see as their most important insights.
Artificial Intelligence is one of the biggest trends to hit BI/analytics, and it’s only getting started. It’s driven by the convergence of a few different trends, creating a perfect storm for AI. Here are a few trends that are driving AI:
First, we have the data explosion. Businesses have more data than they can handle, and are struggling to turn it into anything useful.
Second, we have the talent gap. As data volumes have risen, analytic skills haven’t kept up. Research finds there’s a global shortage of data scientists.
Third, we have advancements in AI technology. It’s getting more powerful and more accessible, putting it within reach of the average company.
Fourth, we have the growing need for instant insights. Businesses don’t want to wait around while analysts try to pull insights out of data. They need answers now!
For business intelligence, AI means a series of narrowly defined computer processes that help augment data with a specific task in mind.
2018 will probably see an increase in the adoption of AI technology for business and an increase in the number of App/AI integrations that make tackling BI problems easier.
A component of artificial intelligence (AI) Natural Language Processing (NLP) is the ability of a computer program to understand human language as it is spoken.
NLP algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples from a book, down to a collection of sentences, and making a statical inference. In general, the more data analyzed, the more accurate the model will be.
Gartner predicts that by 2020, 50 percent of analytical queries will be generated via search, natural language processing (NLP), or voice. NLP will empower people to ask more nuanced questions of data and receive relevant answers that lead to better insights and decisions. Simultaneously, developers and engineers will make greater strides in exploring how people use NLP by examining how people ask questions.
The opportunity will arise not from placing NLP in every situation, but making it available in the right workflows so it becomes second nature to those using it.
Much more than pretty pictures, data visualizations are depictions of information that summarize and explain complex data to a targeted audience. Many people can make data look good. Few can tell you what data means. Fewer still can craft clear and concise visualizations that convey the correct message from their data.
“What I see often are people trained on visualization tools, not analysis,” says Johnny Lee, principal and forensic technology national practice leader at Grant Thornton LLP. “What that begets is an unwarranted trust in the underlying data, and [the] belief that the only ‘analysis’ required for such data is to beautify it.”
As Edward Tufte, whom the New York Times described as the "Leonardo da Vinci of data," says
There are two goals when presenting data: convey your story and establish credibility.
Public cloud services are reshaping the IT landscape. With business success relying on instant access to reliable data, the future data center will need access to the best tools available to compete and therefore the necessity of the multi-cloud data center. Most businesses will need a balance between retaining key capabilities on-premises and simultaneously leveraging public cloud services as a means to lower costs, distribute risk, reduce lock-in concerns, and leverage the latest in cloud capabilities.
According to Gartner, “a multi-cloud strategy will become the common strategy for 70 per cent of enterprises by 2019.”
However, while flexibility is a plus, it increases the overhead cost by splitting workloads across providers and forcing internal developers to learn multiple platforms.
With multi-cloud adoption on the rise, any BI solution you deploy must run on-premise and in the cloud, and port easily between the two. It must have the ability to pull data from multiple locations and transform it into a usable format.
Modern business intelligence means less specialization, more automation, and a free-for-all approach to data analytics as business intelligence becomes highly automated and therefore more easily used by citizen data scientists. As self-service analytics grows, so will end user’s proficiency with the tools and they will move on from simple reports and dashboards into more complex analytics.
However, most executives don’t have the time to sort through data and find insights. They want a snapshot of the data–usually in dashboard form, but most dashboards still rely on the executive to pull the insights out by themselves.
This article over on Occam’s Razor explains why this creates a problem:
“People who are closest to the data, the complexity, who’ve actually done lots of great analysis, are only providing data. They don’t provide insights and recommendations.
People who are receiving the summarized snapshot top-lined have zero capacity to understand the complexity, will never actually do analysis and hence are in no position to know what to do with the summarized snapshot they see.
The end result? Nothing.”
In short, you can’t rely on the executives to pull insights out of data. After all, they aren’t as familiar with the data as the analyst that put it together.
What’s the answer? Provide written insights, actions, and business impact on your dashboards. This image from the article shared above provides a great example.
Not only does this dashboard share graphs and charts, it tells the executive a story. It tells them how the data will impact the business, and recommends action. This is a dashboard that improves decision-making, and the type of analytics that I believe we’ll see more and more of in the coming years.
photo credit: Occam’s Razor
These are just 5 BI trends to watch in 2018, but the list could be much longer. If you would like to add anything to this list, I’d love to hear it. Feel free to share in the comments.