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.
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?
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.
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.
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.
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.
27 Feb 2019
Willingness to start creating Data Lake and Tableau Analytics takes more time than actual creation. Data Lake can be created virtually with any type of data with 100 to millions of records. Tableau Analytics starts when the first record hits the Data Lake. #DataLake #Tableau #Analytics
07 Oct 2019
Hallelujah uses a unique and innovative gamified and tactile employee happiness survey that by itself contributes to serious employee happiness. Data2Performance provides Hallelujah all the technical and analytical capabilities they need to deliver their clients all insights from a Clients' Happiness Survey and recommendations to implement sustainable employee happiness improvements.
19 Feb 2019
Companies need better more flexible upskilling solutions to avoid the risk of skills gap on performance and competitiveness #upskilling #agile #ondemand #agileanalytics
25 Jul 2018
Effective performance management is continuous and agile, enabled by self-service analytics that engages everybody in the organisation every day.
28 Aug 2018
If our on-demand analytics idea intrigues you, your next question is likely: does that exist? The answer is yes!
13 Feb 2019
Agile Analytics empowers us with a set of value driven principles and techniques to deliver analytics insights by deploying visual intelligence, advanced algorithm based analytics & machine learning - deep learning models for any real time business need. Agile Analytics is data driven decision making which is flexible enough to accommodate any sudden change in requirement.
31 Aug 2018
Continuous Performance Management is a top priority for many HR Leaders. We wanted to research how people talk about the move to continuous performance management in blogs and social media.
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.