Manage data analytics like code

Build trust in your data and continuity in your analytics process. How managing data analytics like code help you create more value from your data faster

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Your business and people depend on analytical insight

You know that your business needs analytical insight to improve performance and deliver innovative digital services. Fact is that talent supply in analytics cannot satisfy the growing demand. Developing talent inhouse takes time and comes with an increased risk that talented data scientists and analysts continue their career elsewhere. Therefore, most companies depend on external resources and capabilities to develop and deliver analytical insight from their data.

But there is a problem ...

With every consultant, every service provider, every project partner, at the end of the project the knowledge about your data and analytics walks out of your door. Ask yourself how many analytics consultants, freelancers or project specialists have ever delivered you purposeful documentation about their method, findings and deliverables? Data is our most important asset. Companies increasingly attest to this. If it's true, would you not have to worry more about your data and analytics knowledge management? 

Versioning is common practice in code development.


Versioning, Journalling and Auditing in Analytics.

To improve the continuity and knowledge management of data analytics, we need to ensure that all data analytics process steps and changes are documented and traceable. We need to do that in a way that it adds value and not slows the data scientist and data analytical process down. This comes down to three key aspects: 1. The necessary amount of planning and clarity about objectives and key results and; 2. The right (agile) methods and tooling and; 3. discipline, feedback and reward.

Manage Data Analytics Like Code

Most business applications, data warehouses, BI-systems and analytics platforms have no architecture and functionality to manage records of origin, ownership and history of their data. This is an enormous obstacle to build trust in data and analytics. Most data scientists, analytics specialists and analysts have no skills, tools and traits to manage data and analytics like software engineers manage code. 

Example of a data analytics knowledge ontology:

Knowledge Graph Technology is the emerging standard for companies to manage their digital assets. The technology lies at the heart of the Google Search engine and Wikidata and many more established essential information services. That is why we decided to use Knowledge Graph Technology to help you manage the value and knowledge of your data analytics. 

To build trust in data analytics

The number 1 reason why companies hesitate to use analytics, with the aim to create value from their data, is that they question the quality of their data. Independent metadata knowledge management of your data and analytics help you to develop, maintain and communicate trust in your data and insight.

Your advantage to manage data analytics like code

  1. You obtain a Metadata Common Language
  2. Data and Analytics Versioning
  3. Flexibility and Reproducibility
  4. Continuous Analytics
  5. Risk Management
  6. Insight Integrity
  7. Build Trust

Make analytics agile for everyone

By integrating and embedding knowledge management with every data analytics project we help overcome the discontinuity of knowledge in the consultant approach. 

With Analytics-on-Demand  we are taking this a big step forward because we offer an easy to use and affordable analytics cloud service that has knowledge management fully embedded.



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Manage data analytics like code

Build trust in your data and continuity in your analytics process. How managing data analytics like code help you create more value from your data faster

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Welcome, I am Peter Storm, the founder of Data2Performance. I write about how companies can leverage advanced analytics to move to a continuous performance management system. To be effective in today's dynamic business environment, companies that move to continuous performance management can push their performance culture and productivity to new levels. The primary focus of my writing is how companies can use data, decision and performance intelligence to help their talent and teams increase their performance.