Increase data and agile maturity

Increase data and agile maturity

Increase data and agile maturity 2480 1040 admin

Any organization looking to increase its data and agile maturity should start by creating a data maturity model. This will help to identify where the organization is currently at, and what areas need improvement. Once the data maturity model is in place, it's time to develop an actionable strategy for increasing data and Agile maturity. This strategy should be tied to business goals, and should be led by a senior executive such as the CMO or the CEO. By taking these steps, organizations can increase their data and Agile maturity, and position themselves for continued success.

Increase data & agile maturity summary

Above is an accompanying graph illustrating the five levels of data maturity and agility within an organization. It assists decision-makers in determining where their organization is at and what it would take to move to the next level. The data maturity model has five different levels: datafied, data-driven, insights-driven, hyper-personalized, and predictive. In order to move from one stage to the next, organizations need to have an actionable strategy that incorporates data governance, dataOps, data literacy, and analytics. Moving to a higher level of data maturity requires making data a core part of the business, which can be a challenge for many organizations. However, with the right mix of people, process, and technology, it is possible to make the transition and reap the benefits of being a data-driven organization.

There are different data maturity models that organizations can use to assess their data management practices. One data maturity model is the Agile Data Maturity Model, which has four levels: ad hoc data, managed data, governed data, and optimized data. Organizations at the first level, ad hoc data, have data available within the organization but it is not exploited. The data is used only in a very ad hoc way and decisions are based on intuition, not on data. The data is managed in silos and often each unit manager in the company uses data differently. To move to the next level, managed data, organizations need to develop an actionable data strategy that is aligned with business goals. This strategy should be based on an understanding of how data is currently being used and what could be improved. Once the strategy is in place, organizations need to put processes and tools in place to start collecting and managing data more effectively. This will require some investment in technology and training for staff. However, the benefits of having better data will far outweigh the costs. As organizations move up the agile data maturity ladder, they will be able to make better decisions, improve operational efficiencies, and drive growth.

Level 1


Ad hoc data and agile maturity

First level is ad hoc data and agile maturity. Meaning that you have data available within your company, however, it is not exploited. The data is used only in a very ad hoc way. Accordingly, decisions are based on intuition, not on data. The data is managed in silos and often each unit manager in the company and full-funnel different business units uses different types of data.

No one really knows which data is correct. This is what we call a firefighting database; usually managed in spreadsheets and not in a dashboard or similar. As well there are often no complete funnel measurements, with the result that you don’t really know where or why the dashboards are.

Level 2


Opportunistic and data management

On the second level, it’s opportunism and data management. At this level of your company, there are already some attempts to formalize requirements and measures. Progress, however, is often hampered by culture. There are a few initiatives ongoing, but without a clear structure.

At that level, we often face organizational barriers, maybe IT barriers, different management stuff and different tools. In fact, there’s no design direction within the company. Strategy is not achievable.

It’ s decided again on the basis of intuition, it’s not really effective at this stage too. Data quality efforts are already in place. while measurements are still being made at different levels.  For business units, this is often the case.

Level 3


Systematic data maturity

Now we are dealing with an achievable strategy. The vision begins to be formed and also communicated and used throughout the organization.

Agile and Data use is even now picking up. Internal data sources are linked. Both are integrated, and possibly linked to third party data as well. Finally, information infrastructure is in place.  

Business leaders are progressively turning into Data and Analytics Champions while leadership data is now formed. Thus, we’ re at level three, systematic and data maturity.

Level 4


Competitive data maturity

Now that the data is systematized and mature, we move to the next level: competitive data maturity, we shift to a phase where executives become data leaders. Data champions communicate best practices throughout the organization.

While initiatives are picking up, a Chief Data Officer is appointed. He or she is responsible for  making data driven decisions. So a lot of times when business units need to access data or need to have data insights, they turn to their Data Champion as well. 

We understand, therefore, that data and analysis are the fuel for performance and innovation. Moreover, they link programs and units together and program management is progressing.  We ultimately see how return on investment is based on data.

Level 5


Transformational of data maturity

It’s the Holy Grail. Few companies have reached this level so far, but it is certainly a source of inspiration. Such companies are the ones where you see that data and analysis are at the heart of the business strategy. So, a business strategy is based on data and data also influences marketing investments, afterwards strategy and execution in turn are aligned between business units. Business strategy is continuously improving and data-driven.

Such companies therefore have an outside perspective. They have achieved a Data Officer whose role is mostly to be a member of the board of directors as well. Therefore, it is someone who is critical to the strategy, so they use predictive analytics in all business units. Everyone applies the same KPI. 

 As we can see now, information and data are a strategic asset for the company. On this basis, they are one step ahead of their competitors as well as their business strategy is based entirely on that data.


Conclusion

This is a brief overview of how we classify companies based on data and agile maturity. And as a growth agency, our primary goal is to help companies move through these different levels.

Realistically, moving from one level to another takes companies about a year, even several years, for some companies. This will differ according to the infrastructure, company age, whether the different business units are managed, and so on.

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