I. Basic Information on the Test
Some time ago I have written about the stage of development for organizations with respect to their data strategy.
The following questionnaire is a toy-model that could help managers to grasp a rough idea of the current data stage of maturity they are facing within their organizations. It has to be integrated with deep conversations and meetings with the big data analytics (BDA) staff, the IT team and supported by solid research. Choose only one answer per question.
- What is your investment level in BDA capabilities?
1. Absent. We don’t have money for big data
2. A small budget is allocated when positive quarters in core activities allow us to do that
3. A modest funding scheme is in place
4. We invested a good percentage of our revenues in BDA in the last year, and we will keep investing because it is part of our company’s vision
- What executives’ support to analytics capabilities look like?
1. Neither IT nor business thinks BDA is useful to the business
2. Only IT managers support it because they are interested in the technological challenge
3. Business managers see the hidden value in data and support BDA projects
4. Both IT and business executives believe in BDA potential
- What is your current stage of working with data?
1. We will start using data in the future if needed
2. We have a good idea of what business questions we could solve with data in our company
3. We take action using analytics
4. We are automating analytics as much as possible, and we believe is a competitive factor that gives us benefits we will be able to communicate frequently to top management and shareholders
- Your analytics team is:
2. Outsourced at the moment
3. We only have some senior scientists that have been recruited, but we are now growing the team internally by training
4. An independent sustainable group and function within the company
- Your company’s culture is:
1. Intolerant, especially for failure concerning new analytics, methodologies, and new technologies implementations
2. Variegated. It is half-half made by old-style professionals and geeks
3. Collaborative. People are willing to work together and share.
4. Creative. Innovation is valued and we are encouraged and monetarily compensated for sharing our original contribution.
- How is your data science team connected to the company hierarchy?
1. We only have some analysts with small tasks, who deliver the outcomes to their direct managers on a regular basis
2. The data team is led by a business head, and their contribution is continuously marginally positive
3. Our data scientists are tight to our data warehouse and data management teams, and they constantly interconnected with the business side
4. They are autonomous and do not seat in the same building of the operations function. They are allocated in a Centre of Excellence.
- The internal data policy is:
1. Fairly poor, we do not need it
2. Metadata definitions and BDA policy are well-established
3. We have a BDA policy that we constantly monitor and we have a security policy for any data form
4. We have a BDA and security policies, and we anonymized all the relevant data to protect our clients and partners’ privacy.
- The data in your company are:
1. Stored in silos
2. We prioritized the data to be used within our organization, and they are internally shared
3. Many different data sources are integrated for our analysis, and we take care of data quality through a meticulous goodness assessment based either on the final use or the type of data we will exploit
4. We have integrated BDA technologies into our systems, we store our data on a cloud, and we often use them for mobile applications
- When your company looks at its BDA capabilities:
1. It sees mainly a sunk-cost, i.e., the cost of storing, maintaining, protecting and analyzing these datasets
2. We know data have value and we understand both the data cost and data competitive advantage, but we are definitely overwhelmed
3. We are rationalizing our data storage and usage abilities because we understood that not everything is either pertinent or meaningful
4. We have an efficient process for data aggregation, integration, normalization and analysis, and we can manage easily any amount of inflowing data.
- Your firm is currently using:
1. Relational Database and Internal data
2. Data marts, R or Python languages, and public data
3. NoSQL database, Hadoop and MapReduce, and we use external data, sometimes also unstructured
4. Highly unstructured data, APIs, and a Resilient Database
III. How to Read the Test
Once each single question has been answered, it is simple to obtain a rough measure of the data maturity stage for a certain company. For each answer indeed, it has to be considered the number associated with that answer, and then it would be enough to sum up all the numbers obtained in this way. So, for example, if in the third question the answer is “we take action using analytics”, the number to be considered is 3, since it is the third answer in the list.
Finally, the score obtained should range between 1 and 40. The company will then belong to one of the four stages explained in Table below accordingly to the score achieved, which is explained in this previous article.
Note: the above is an adapted excerpt from my book “Big Data Analytics: A Management Perspective” (Springer, 2016).