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How To Govern Data With Machine Learning Tools

The importance of mapping the information present in the company and managing its completeness and quality is not questioned. The saying “garbage in, garbage out” is today more than ever a truth that must be contained within contexts where the data lake incorporates more and more information.

For this reason, it is necessary to intervene promptly and overcome manual information mapping activities that bring with them the operational risk of manual management or inefficient timing. From this point of view, machine learning algorithms can significantly help information management, understanding the use of data, and understanding the fascinating information assets on which business decisions are based.

Companies that have understood the importance of data and have made it a strategic corporate asset by becoming actual data-driven companies must today take into consideration and monitor some main aspects:

  1. How are the different functions using the available information assets?
  2. Is there a homogeneity between the numbers that the various internal stakeholders use to make decisions?
  3. Are all management reports consistent in terms of reference perimeter and business logic managed?
  4. Within the individual working groups, how can we make sharing information easier through constantly updated data literacy?

FAIR: Follow An Open Reference Framework

In particular, these principles touched on four main levers: Findable, Accessible, Interoperable, and Reusable. These principles emphasize the ability of machines and expert algorithms to make information usable even when the volume, complexity, and speed of production of new information increase. In a scientific community, these principles can be considered the basis of an open approach to collaboration.

However, it isn’t easy to think that these principles should not be present in business collaboration contexts or, thinking of the world of Open Innovation, in the context of co-innovation between complementary realities that can make data exchange an asset of two-way value. Let’s go into detail:

  1. Findable – Discoverable: To use the data, it must be found. Both metadata and data should be registered and easily identifiable by computers and people. In particular, metadata plays a fundamental role in automatic discovery by the machine and the activation of a human-digital process.
  2. Accessible – Accessible: the data must then be accessible to interested users, knowing how to access them and at the same time guaranteeing respect for privacy according to authentication and authorization protocols.
  3. Interoperable – Shareable: data must be able to be read and integrated by multiple stakeholders. The ability to connect them to different systems, enable them to numerous business processes and homogeneously archive them allows for efficiency.
  4. Reusable – Reusable: it is the final goal of the reference framework. Optimize data reuse through the correct management of metadata and intelligent information reading systems.

These principles represent a good starting point for enabling governance of the data of value. Starting from these principles, the Global Indigenous Data Alliance (GIDA) also published new CARE principles, complementary to FAIR, introducing Open concepts concerning control authority, ethics, responsibility, and collective benefit.

Govern Data In The Age Of Data Intelligence

Starting from the principles described above, various projects for data sharing and management were born. Among these, one of particular interest can be considered OPAL. A platform with technical components based on available algorithms aims to enhance private data for the common good through respect for privacy and a sustainable approach.

With a focus on applying these principles to business contexts, the main benefits that a flexible, open, and sharing approach can bring to companies are shown below:

Greater Operational Efficiency In Terms Of:

  1. Less time spent on data discovery
  2. Less time for data training on new colleagues
  3. Continuous updating of data and metadata

Greater Effectiveness For Data Analytics:

  1. Suggestion to the user in terms of choosing the correct information for the analysis of interest
  2. Strengthening collaboration between colleagues from individual company functions
  3. Suggestions and tips regarding the thematic area of ​​analysis

Machine Learning:

  1. Artificial intelligence systems learn over time from data analysis by classifying information in automatic mode.
  2. Data similarity based on machine learning, or the understanding of which information contents can be considered similar, complementary, or redundant

Application Scalability:

  1. Insight first approach on all the analysis entities of interest
  2. Ability to easily integrate any new data source

Some of the features of most significant interest in artificial intelligence tools can be:

  1. SIB (statistics insight box): modules for the description of the contents of the surveyed tables and identification of the primary information relating to the information content, such as primary and foreign key, type of data, size of the table, update frequency.
  2. QAS (query analysis system): query analysis system that automatically analyzes users’ frequency of use of the tables and information in the DBs. It uses machine learning algorithms to interpret the parsing and analysis of the content of the queries logged in the various business systems. Furthermore, it exploits a classification of the domain and semantic sub-sets to identify the most helpful data areas to end-users.
  3. DEE (data enrichment engine): engine for the continuous generation of an enriched information heritage that describes and classifies tables, columns, and data entities concerning the various characteristics, such as distribution values, type, size, additional available metadata, etc.
  4. SIM (similarity index machine): machine learning engine that enables similarity metrics of the columns analyzed in the company DBs, to identify similar elements used in different semantic contexts. The machine integrates various statistical and mathematical techniques such as unsupervised techniques and recommendation engines (e.g., Netflix).

Also Read: Machine Learning Applied To Industry 4.0

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