Maturity of Data and Analytics
In this blog, we will discuss two generally applicable models that help you determine in what stage of data maturity your company currently resides. Afterwards, we will have a look at the adoption of AI and Data Science around the world.
There are often many ways to model a situation. Each approach has its up and downsides. In this post, we will take two models created by Gartner and explain how to interpret them. These specific models have been selected as they give a good and intuitive insight into the matter that is discussed without being overly complicated.
In addition to that, we will use a fictional logistics company, “TRANSPORTD”, to clarify various concepts throughout the post.
Gartner’s analytics ascendancy model
A model that is used by a broad selection of literature is Gartner’s analytics ascendancy model.
The model identifies four types of analytics:
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
As the name suggests descriptive analytics uses data to describes what happened in the company. It is often used to get an insight into the performance of the company in the past.
These analytics include very simple cases that carry little value for the company. For example, for TRANSPORTD this can be an overview of the number of trucks per type (e.g. long-range hauling, short-range hauling, heavyweight or special transport etc.)
A more valuable insight for the company can be the number of hours lost in delays. This gives an employee of TRANSPORTD more insight into whether a route profitable or not. But also, may help indicate whether a route can be optimized better.
The second form of analytics described by Gartner is diagnostic analytics. In this analytics-stage, the focus lies on diagnosing problems or occurrences in the data. The analyst will try to figure out why these hours are lost. Is it due to traffic jams? Or are loading times longer than normal? Knowing why things happen allow the company to adjust its operations to improve the situation. In our example of TRANPORTD, if loading times are a major cause for delays then measures such as an extra forklift truck on the site may reduce delays and therefore reduce costs.
Thirdly, there is predictive analytics. This segment of analytics deals with making predictions on what is going to happen based on previous data. When looking at our fictive transport company we can introduce some forecasting methods that help TRANSPORTD in saving costs. One of the many options would be to predict how many packages need to be delivered for a specific day. This helps the planning department to staff enough people for the day and decrease the number of people that are required to be on standby.
Last is the most valuable form of analytics; prescriptive analytics. This segment of analytics revolves around prescribing decisions and actions to the business. This is both the hardest and most valuable form of analytics. For our logistics company, TRANSPORTD, we can find use cases such as automatic route planning. A solution where an AI decides which truck should deliver to which address. The AI divides the addresses in such a way that it minimizes factors such as costs and CO2 emission.
Since each type of analytics has its own difficulty it is a good indication of how advanced your company is on the field of analytics.
More difficult techniques require that your data availability and quality must be very good in order to succeed. In other words, your company is very mature in its transformation to data-driven operations.
Gartner’s model on data maturity
Gartner also presented a model specifically for the maturity of data and analytics in a company.
Based on this model, can you determine at what stage you company is?
Gartner’s study on data maturity
Gartner’s study in 2018 concluded the following:
“The majority of respondents worldwide assessed themselves at level three (34%) or level four (31%). 21% of respondents were at level two, and 5% at the basic level, level one. Only 9% of organizations surveyed reported themselves at the highest level, level five, where the biggest transformational benefits lie.”
Compared to the Asia Pacific (APAC) and North America, the EMEA region seems to be lacking in its digitalization transformation.
Only 30% of companies in EMEA (Europe, Middle East and Afrika) reported being in the top 2 levels of maturity whereas APAC and North America got 48% and 44% respectively.
That being said, the transition to data-driven operations is going slow all over the world. It is time to step up and start digitalization now!
If you are unsure of where to start, COMPUTD will gladly discuss the possibilities within your company without any obligations.
Perhaps a good starting point may be our “What’s your data worth” service.
If you already have an idea on what your next digitalization step should be, we are happy to see how we can assist!
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