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In 2020, all the studies predict the huge growth of data. Thanks to IoT devices like smart sensors, wearables, smart toys, smart cities technologies. With this environment there are a lot of challenges for the organizations, but the main is to transform all these data into knowledge to be more competitive or at least survive. Because if you do not do that, your competitors will advance you.
Actually all papers are talking about topics like Data, Insight, Goals, Cloud, IoT, Big Data, Small Data, Dark Data, IA, Deep Learning, Machine Learning and so one. But which is the global word who drive all of them?
- In 2015, the top ‘barrier to success’ cited by Business Intelligence and Analytics Departments is not the ‘Data Quality’ (with only 51% of technology professionals choosing it as a ‘barrier’ & the value have diminished 8 points (59%) from 2014). Reference: “Doug Henschen. (2015) 5 Analytics, BI, Data Management Trends For 2015”.
- In 2017, Gartner mentioned that the average business wastes as much as $13.5 million sorting out data quality problems every year. Reference: “Martin Doyle. (2017) How a Chief Data Officer Can make Your Data Great”.
- In 2017, Valerie Logan, research director at Gartner, said: “We are seeing the data officer is growing. There are 3,000 to 4,000 CDOs in the market globally. It is a role that will persist”. Reference: “Cliff Saran. (2017) Chief data officer role is key to business success, says Gartner”.
- In 2018, Susan Moore said: “Poor data quality destroys business value. Recent Gartner research has found that organizations believe poor data quality to be responsible for an average of $15 million per year”. Reference: “Susan Moore. (2018) How to Create a Business Case for Data Quality Improvement”.
As you can see, the Data Quality is the main topic for Digital Transformation, and is directly related with how the organization can obtain knowledge from data. Now that you know this, do your company need to waste more money before starting with Data Management? I don’t think so.
But what does Data Management mean?
The definition provided by DAMA© International is: “Data Management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets”.
And it includes the following disciplines involved with data:
| 1. Data Governance
2. Data Architecture, Analysis and Design 3. Database Management 4. Data Security Management
|
5. Data Quality Management
6. Reference and Master Data Management 7. Data Warehousing and Business Intelligence Management |
8. Document, Record and Content Management
9. Meta Data Management 10. Contact Data Management |
| Source: DAMA DMBOK Framework | ||
Which roles are needed?
The main role related with data management is CDO (Chief Data Officer). He is responsible to build the environment and implement best practice in all the disciplines before commencing. This new role is growing since last year, in 2017 the percentage of CDOs increased seven points, from 50% in 2016 to 57% in 2018. The number of CDOs will be higher as per Gartner’s 2017 “Gartner Chief Data Officer” survey.
Gartner predict 15 percent of successful CDO will move to CEO, COO, CMO or other C-Level position by 2020. You can imagine how import it is. Of course, for CDO – there is a small office for it with 24 people, and the roles of the team are:
| § Data Analyst | § Data Engineer | § Data Custodian |
| § Data Owner | § Master Data | § Data Scientist |
| § Data Architect | § Business Intelligence |
What we can do to implement it?
The first step is to evaluate the current status of our company. There are 5 levels of maturity for Data Management using DAMA classification. In which level is your company?
| Level | Maturity | Features |
| 1 | Initial | There is a data authority in the IT department, but it has little influence on business processes. The collaboration between IT and business areas is not consistent, and there is total dependence on certain data experts in each business area. As a result, the processes are not integrated within the organization |
| 2 | Managed | Owner and administrator are usually present in lines of private businesses. There are poorly defined processes in applications key in the business lines, and the data problems are managed in a reactive way, without identifying the source of the problem. Is an early stage when standardizing the processes in the different business lines |
| 3 | Standardized | The business is involved, there is a team of different functions, as well as data managers with responsibilities clear. There are established standardized processes and consistency in the business lines. There is a central data policy repository for easy access, and the quality of the data is regularly monitored and measured |
| 4 | Advanced | The organizational structure of the management of the data is considered as a critic for all the functions of the business. The business is the owner of the content of the data and the creation of the data policies |
| 5 | Optimizing | The data management is a central business process, and business decisions are made evaluating the benefit, cost and risk. Process improvement objectives are created for the organization, which are continuously review to reflect possible changes in the objectives of the deal. The costs are reduced, due to the automation of processes |
| Source: “Marcos Pérez González (2018) El Ciclo de Vida del Dato” | ||
The second step is to create a workflow including all the process, people and technology that have to be applied to go ahead. Data management is supported by these three pillars.
- People: the processes and data management tools are as effective as the people who use them and manage them. The first important step is to establish a team of individuals of the organization and equip them with clearly defined roles and responsibilities & as well as resources to perform their required functions. Also, provide guidance on the general objectives of data management.
- Process: with the right people involved, the organization can focus on defining the processes involved in data management. This starts with the examination of several authority documents (statutes, regulations, norms in a field of organization and state policies, company policies and strategic documents) that specify the requirements that must be met.
- Technology: the next step is to apply the tools to analyze the flow of data and identify the risks and apply the necessary measures of data management.
The third step is execution- you have to apply the workflow before defined, using the company resources.
The fourth step is measure – you have to define all the metrics and monitor it to compare between the value achieved and committed. You have to identify deviations, issues or non-conformity and be compliant with the workflow committed.
The fifth step is review – and you must implement all new capabilities and improve to be more effective and efficient in increasing value for the organization.
Which are the benefits?
- Improve the Data Governance
- Optimize the ETL process
- Increase the productivity
- Avoid or at least minimize security and privacy issues
- Only one repository, no more silos
Sources:
http://www.dataversity.net/chief-data-officer-challenge-build-data-strategy/
https://en.wikipedia.org/wiki/Data_management
https://www.gartner.com/newsroom/id/3509918
https://searchcio.techtarget.com/blog/TotalCIO/Gartner-Chief-data-officer-role-continues-to-grow
