NEWSFLASH: static Excel spreadsheets pulled from a complicated ERP system aren't Business Intelligence.
Here's a startling fact: There are more businesses who don't yet use a comprehensive business intelligence (BI) solution than those who do. These companies aren't necessarily clueless or underperformers. Many large, international companies are at this stage. Rather, they're using the same sorts of management information systems today as they did 20 years ago: standard reports produced via their ERP systems and static analyses created in Excel. Newsflash: this isn't business intelligence.
Speaking in natural language into your phone and watching an analysis come to life in real time is: that's just the beginning. But to get from Point A to Point B isn't something that happens overnight.
It's a three-step journey that takes companies from basic BI to building new strategies by looking outside the company and tapping into the available ocean of data. That's what we call being analytics-driven. The journey progresses along with the increasing amount and complexity of data able to be analyzed. The depth of analytics grows throughout the organization, spurring more frequent data discovery and improved analytical skill sets. Progression along the path toward the data-driven business is also one of cultural transformation. Decision-making processes across the organization become more strategic and fact-based.
I'll quickly walk you through this process below, but you can get an even more detailed picture from my free ebook, "3 Steps to Becoming an Analytics-Driven Organization" and my upcoming free webinar on June 18 from 11 a.m. to 12 p.m. EST, "The Data-Driven Journey." I hope you can take advantage of these resources when planning your own future strategy.
Step 1: Harnessing the power of your data (basic BI and Analytics)
The first step on the journey toward data-driven performance is a significant one: getting access to your data. With a BI solution, you can tap into the highly valuable knowledge that was already at your feet but was hard to crack into due to a cumbersome IT process.
With the right BI platform, you can get up and running almost instantly with acceleration packages. Pre-built data stores come standard and users experience easy adoption with pre-defined reports, dashboards, and analyses.
This operational level of BI involves the incorporation of the data sources that are most critical to the business. Basic reports and analytics are pulled easily. Management has committed to a unified version of the truth based on approved data sources, and starts to dig into internal data to reveal new kinds of actionable insight.
At the beginning of this stage, analytics is often the realm of a few super users in IT or finance. As the organization becomes more mature in BI and analytics, standard reports give way to specialized dashboards and reports. A cycle of better insight leading to better questions and answers begins. Users become fluent in creating multi-dimensional analytics.
Information is easy to disseminate throughout the organization with dynamic storyboards and mobility features so information remains consistent and up-to-date. By the end of this stage, an increasing amount of people in an increasing amount of departments are embracing BI and analytics. At a basic level, fact-based processes and decision cycles are on the rise.
Step 2: Executing strategy while seeking operational excellence (real time data discovery and action)
As the organization grows in analytics maturity, data sets get broader. The volume of structured and unstructured data being analyzed increases dramatically to include sources outside the ERP system. This is the time to take what you're learning from your data and carry out your strategy. Think of it as the link between learning and doing. And analytics is accelerating that learning process.
As data volume, variety, and velocity increase so does the organization's ability to use it. Analytics resources and skills are no longer limited to a few departments. Everyone from sales to marketing and supply chain is leveraging the benefits. Users across the company become smarter about the entire value chain. Learn from what you're analyzing and improve your system.
Decision making is spread throughout the company because business intelligence is arming users with the information they need to make decisions and take actions quickly and with confidence.
Analyses are increasingly used to strategically plan for the future, not just examine the past. Automated notifications based on multi-dimensional analyses keep people informed of key developments within their KPIs without keeping them glued to the screen. Users can proactively make real-time operational adjustments. Predictive analytics learns from the past to provide educated guesses about probable futures.
Users at this stage have begun to take advantage of some of the more powerful BI tools. Ad-hoc analytics give instant answers to variables inside and outside of the data warehouse. And user-friendly dashboards help make creating and sharing comprehensive reports and analyses easy.
Step 3: Shaping future strategy (competitive analytics)
This is the most strategic step. Companies operating at this level of maturity employ the most highly skilled analytics resources they can find. Analytics is used to formulate the future strategy, and new KPIs emerge and are put to the test. Current strategies are adjusted and are changing based on new knowledge found within analyses.
More and more external data is used strategically. There is a vast number of data sources that are outside of your control in terms of data availability and quality, but they are highly valuable. This is the so-called Big Data. With resources like Google, Twitter, Facebook, and blogs at every consumer's fingertips, it won't be long before a company's external data far exceeds their internal data.
In many cases, that data imbalance means customers are forming their opinions about your brand before they even come in contact with you. In fact, 60-70 percent of customers say their buying decisions are made before even talking to a vendor.
Analytics makes it possible to know what customers are saying and searching for by providing an easily digestible snapshot of positive/neutral/negative speech about your company, products, and competitors happening on Twitter, Facebook, and LinkedIn. This type of analysis is the fastest route to understanding what's working in the market. Only here can you listen for the questions you didn't even know to ask.
At this level, there is a dynamic integration of BI. Organizations are contending against the competition based on their analytics. Internal and external data sources are at play. This is a true analytics-driven culture.
Companies don't progress from stage one to three without spending time in between. It's critical to strive for incremental, evolutionary improvements that are achievable, rather than revolutionary leaps that promise fantastic results but also require significant shifts in corporate culture.
Another lesson learned is that moving from one stage to another requires organizations to change. And as we all know, change isn't easy. I've seen too many companies evolve from level one to level two only to see inertia regain control. Instead of continuing to push for greater analytics power throughout the organization, they level off in what becomes a new comfort zone.
Adaptation is the key to survival in the business jungle. The courage to act is also the courage to change old habits -- and keep progressing on the path toward the data-driven business. And don't forget, you can read more about becoming an analytics-driven company in my recent white paper, "3 Steps to Becoming an Analytics-Driven Organization." Download it for free.
And join me at my upcoming free webinar on June 18 from 11 a.m. to 12 p.m. EST where I'll talk about this and how you can apply it to your organization to shape your analytics-driven strategy. Register today!
Dr. Morten Middelfart
Founder and Chairman of Social Quant
I've been working professionally in the software industry since I was 14 years old, and my passion for computers has never stopped growing. Today, I'm deeply involved in educational activities that advocate my research within business intelligence and analytics. By the time I was 25, I had established Morton Systems, my first business intelligence and analytics c..