Analytics maturity assessment

The written descriptions of each category are presented in the Appendix F. Aim of the thesis was to pick a realistic target state in order to be able to assess the capability to deliver different analytics services. The targets should be revised after making improve-ments to capabilities since these are the initial targets. Target state is hard to assess as there are multiple earlier separate companies now under the same management. Target state is also affected by the vision of what kind of services should the organization be able to deliver in the future and so, at what level should the capabilities be to achieve this.

Capabilities describe the analytics maturity in the organization and so is directly tied to how well the organization can deliver services when moving higher in the value chain.

Operating in the higher levels as a service requires more systematic approach and em-ployee commitment to the data and value-based approach.

Vision

Level 3: Proactive was chosen for the vision based on the need to enable business users across the organization to utilize the future solution. The earlier levels are focused on just the operational needs and IT lead business intelligence is somewhat siloed. Breaking those silos and enabling all the users is the number one priority. Using external and dif-ferent types of data together and understanding the need for data in order to create the wanted outcomes is something that should be achieved. The higher levels require organ-ization to be more data-driven for the information to fuel all the processes. The day-to-day work is still operational, and all the customers do not gain so much value from the information as the customer segments can be far from one another in terms of value per-ception.

Strategy

Level 3: Proactive was chosen to produce more dynamic and responsive organization wide strategy to manage data assets. Centralized assets are shared in the organization and C-level sponsor is coordinating the organization wide strategy. The higher levels are not fulfilled as there is no real CDO office and the analytics operations are still separate tasks.

The lower level functions such as individual source of powers and purely technological focus is solved.

Metrics

Level 3: Proactive was chosen since the financial justification does come with a direct benefit in mind. Qualitative measures for the analytics operations are understood as the value created is not always financial. The lower level problems such as minimizing ex-penses and subjective goals are not the focus points. Of course, people are biased but the main idea is to enable a larger number of people for multiple tasks through data and ana-lytics. The higher levels require information management to be its own managed metric itself. Information related metrics are tied to business metrics and the value of information is measured and tracked.

Governance

Level 3: Proactive was chosen to support the integrated MDM and metadata programs.

Data is standardized, and quality is mostly automated. The lower level shortcomings such as information silos and data trust issues are solved. However higher-level issues are still there. Policies are set based on the business needs, but they reflect mostly from primary processes. Same policies are not carried through projects. Focus in governance is to have centralized data governance but hand out local stewardship of data.

Organization and roles

Level 3: Proactive was chosen based on the current roles and how organization is orga-nized. The data modeling is mostly done by IT department and the specialty roles are housed in the IT department. Business units are raising the skill level through technology to enable self-service. There are not many in-house data related roles but the need for them in the future has been recognized. Many of the roles are still outsourced. The ana-lytics overall maturity affects the need of professional anaana-lytics roles as when the analyt-ics is managed in high level, the business users should be able to act without analytanalyt-ics professionals.

Lifecycle

Level 4: Managed was chosen in order to reflect the need of automating the data lifecycle because of the self-service goal. The focus is enabling the data and information across the organization while making sure that the data stays relevant. Information architects are used in larger projects, but they are not embedded in maintaining. End-to-end lifecycle is not widely established. Standard procedures are used across organization and architecture drives the processes and policies. Shortening the length of process from gathering the data to be able to use it, raises efficiency.

Infrastructure

Level 4: Managed was chose due to the intended new infrastructure where infrastructure is a centralized cloud-based solution. The infrastructure is an enabler, not just a tool. The processes rely heavily on utilizing the infrastructural possibilities to enable business users to use the solution to be able to cover some of technological shortcoming of the user. The investment is a solution investment and not just purchasing a tool for a purpose. The solution covers known business needs and analytics capabilities are built to cover organ-izational needs. The technology cannot be the only enabler of capabilities, but infrastruc-ture should be leveraged according to the other capabilities.

Summary

The average score for the company X’s target state is 3,3. All the categories have the same weight to calculate the average. The higher targets are on the more technical cate-gories and the reasoning behind that is to have higher capabilities through technology and automation through technology. The summary of categories and target levels is presented in table 3:

Table 3. Analytics maturity assessment

Category Target level

Vision 3

Strategy 3

Metrics 3

Governance 3

Organization & roles 3

Lifecycle 4

Infrastructure 4

The score would place the company X in the upper half of the organizations of the study presented in the literature review. As this is the initial assessment of where the capabilities should be aimed in order to be able to deliver intended services, the score is in line with the services. The average goes under the description “proactive” that describes well how the organization should manage their data and information assets as whole. Next level is managed but only 20% of the research’s companies are placed in the managed category.

The fact remains that the company X is not in IT or data related business on day-to-day

context but how the created value is communicated to the customer, heavily depends on business intelligence.