In today’s digital world, it is hard to think of instances where data is not being collected and leveraged by businesses, small and large.
With the rise of technologies like fitness trackers, activities as mundane as walking are now producing important measurable data sets — and the IoT market is growing every day. So the volume of data is only going to grow rapidly.
Boundless supplies of data, advanced analytics and new artificial intelligence (AI) capabilities are putting pressure on enterprises to accelerate their digital transformation as new areas of competition are constantly created. Forward-thinking organizations are already harnessing advanced analytics to upsell and cross-sell, optimize operations and run their own systems and processes more efficiently — but all of this requires a certain level of analytics IQ.
What is analytics IQ?
Analytics IQ is a measure of an organization’s ability to leverage analytics to support business and IT objectives.
Many organizations start their analytics journey eagerly, but without a clear strategy. This approach often leads to failed pilot projects, which have not provided the needed insights to answer business questions.
Let us take a step back and first focus on analytics.
It is easier to understand analytics when you understand the process that data goes through to become actual, actionable intelligence, rather than unusable numbers and words. I like to think about it in terms of retail. The price of an item is just plain data. However, when we add additional indicators, e.g., the price is attached to a celebrity’s merchandise, and recently, that person was involved in a controversy — then this data becomes information, something of interest to us.
The information can then be used to try and predict what will happen to the price of this merchandise in the following days. That is intelligence: When we add context to information, it becomes intelligence.
To enable businesses to effectively leverage analytics, a company must have the right people, processes, and technologies in place to extract this kind of intelligence from all the data being collected. As some enterprises have stronger resources in one area than another, it is important for companies to double down in their area(s) of strengths, e.g., their people or their processes.
Additionally, companies must also acknowledge their weaknesses, which could be their technology. No matter where the deficiency lies, strength often lies in having good partners. To bridge a technology capabilities gap, successful enterprises team up with external technology partners to help deploy digital solutions at scale and produce better business outcomes — ultimately raising an organization’s analytics IQ.
How to measure analytics IQ
Analytics IQ can be measured in terms of the maturity of a business to leverage analytics. Most enterprises already have some form of analytics taking place within their IT departments, but companies are not all at the same maturity level when it comes to using this intelligence. Maturity defines a company’s ability to use their analytics to predict and define business outcomes.
Typical models may classify analytics maturity, or analytics IQ, as follows:
0: No analytics capability
1: Descriptive analytics: A company implements business intelligence (BI) solutions and generates reports with key performance indicators (KPIs) demonstrating how the business is performing. The company then analyzes the reports and makes changes accordingly, to increase the business value being delivered. Most organizations are at this level of analytics maturity.
2: Predictive analytics: A company collects past data; and through a combination of AI/machine learning (ML) and BI techniques, the company uses this past data to predict business performance.
3: Prescriptive analytics: A company uses past data and builds AI/ML models that not only predict what, when and why something will happen — but also present decision options that can help seize future opportunities and mitigate risks.
Benefits of a strong, strategy-driven business analytics IQ
The ability to develop intelligence from data helps companies stay many steps ahead of their competition. By successfully harvesting intelligence from vast troves of data, enterprises can improve productivity, reduce costs and increase competitive advantage. Organizations that do not have a high analytics IQ will remain stuck in the realm of ad hoc analytics, characterized by many repeated proof-of-concept exercises and pilot projects with no visible business outcomes.
Whether a company has a center of excellence that completely covers its analytical needs, or brings in a third-party services provider to offer expertise, strengthening a company’s analytics IQ can take the business from simply counting steps — to predicting its next steps on the path to success.
