Data is a critical competitive factor and is becoming more and more crucial for achieving success. In theory, hardly anyone disputes this to any extent. But in practice there are some very different levels of maturity when it comes to handling data. Based on practical experience gained in medium-sized companies in particular, I would like to try to describe five typical levels of maturity. This is intended to provide a helping hand on the way to becoming a data-driven organization.
The focus of the following maturity levels is on how data is handled. This is related to the topic of Business Intelligence (BI), but it is not identical. In my opinion it emphasizes something different, something more modern. Various maturity models have been put forward in the context of BI (including those by Gartner and TDWI), but these frequently focus on developing from a report-oriented organization to advanced analytics (taking an expert look into the future).
There is a wide range of systems, each of which administers data, but they are not extensively interlinked with each other. Typically, there is one, or several ERP systems as well as a variety of “tools”, which increasingly include solutions in the cloud. Each of the systems usually provides isolated reporting, data is often interlinked by hand using Excel and evaluations demand a large amount of manual work. Data silos have emerged and knowledge is fenced off in “territories”. Evaluations are primarily retrospective. The company finds it hard to use data to help them take a competent look into the future.
The data landscape comprises data islands – bridges have to be built by hand.
Based on the islands described above, individual bridges have been built which connect the data in various systems. This means there are several comprehensive reports, but answers to questions based on data are only available to a limited extent (and mostly only for just a few users). Excel is still considered to be significant for evaluations. There are redundancies in the data, inconsistencies and frequently it is necessary to rework the data retrospectively due to insufficient data quality. Reports can be conflicting depending on who creates them. Any bridges are usually programmed by hand and are fragile.
The data landscape comprises islands which are networked via fragile data transportation.
A central data warehouse has been established which data from all relevant systems flows into (including data in the cloud), in addition to the ERP, thus providing a secure up-to-date data inventory for the most important aspects of the company. On the whole, access is strictly regulated, some standardized reports are available on the basis of the data warehouse. For certain questions, users are provided with selected parts of the data inventory, for example via a data mart. Work is in progress on improving data quality. This maturity level correlates to the traditional understanding of business intelligence.
In this data landscape all the islands are connected with a central data warehouse which the data is transferred to.
There are standardized tools for accessing data, which – according to the roles allocated within the company – staff can use to answer questions, gain knowledge and to access preprocessed evaluations. The staff are sensitized to the significance of data. Links with external data are possible. Self-service analysis programs such as Tableau or Qlik are in use and utilize central data. Internal discussions about data strategy are primarily concerned with the question of which additional data is required and are often about the challenge as to how these can be made available in (almost) real time. Hypotheses are formed which can quickly be verified or disqualified using data.
Knowledge workers have access to self-service for data and analyses, the company learns from data.
Within the culture and self-concept of the company, data are seen as a significant asset, i.e. they represent an economic value for the company in the same way as their customers and products do. The potential of data is monetized, it is the source of innovation and the basis for decision-making; its significance is recognized throughout the whole company. Everything within the business that is able to supply data, really does supply data. The use of data is experimented with, positive aspects are enhanced, failures accepted. The company uses every additional data set to train and develop its decision-making skills. Recurring decision-making processes have been automated. The company concerns itself with topics such as machine learning and systems with artificial intelligence.
The whole company (also) revolves around data, it’s work is truly driven by data.
When planning the next part of your journey it helps to determine where you are now. The maturity levels outline the next steps along the journey. As mentioned at the beginning, nobody really disputes the fact that data is relevant for the future competitiveness of companies. The journey is clearly worth it. Of course there are obstacles and challenges along the way – of a cultural nature as well as organizational and technical ones.
Modern technical tools are essential to ensure the journey is comfortable. The Talend platform is an efficient ally which grows in line with the level of maturity – from data integration through application integration, data quality and master data management to cloud integration, big data integration and meta data management and finally to self-service for data preprocessing.
Can you find your level of maturity here? How are you planning your journey?
About the author Dr. Gero Presser
Dr. Gero Presser is a co-founder and managing partner of Quinscape GmbH in Dortmund. Quinscape has positioned itself on the German market as a leading system integrator for the Talend, Jaspersoft/Spotfire, Kony and Intrexx platforms and, with their 100 members of staff, they take care of renowned customers including SMEs, large corporations and the public sector.
Gero Presser did his doctorate in decision-making theory in the field of artificial intelligence and at Quinscape he is responsible for setting up the business field of Business Intelligence with a focus on analytics and integration.
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