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How Data can be tamed with DataOps

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Companies are already selling and purchasing data for years and are generating revenue out of valuable data. Some very interesting facts about data

  • Google handles 3.8 million searches per minute on average across the globe
  • Twitter processes around 350,000 tweets per minute
  • Facebook gets 243,000 photo uploads per minute
  • LinkedIn users publishes 130,000 articles on weekly basis
  • Instagram receives 300 million photos per day

These are just a few stats representing the business model these companies operate on. Let us now look into the day-to-day communication medium that we use in the office – “Email”. The average office worker will

  • Receive around 121 emails per day
  • Send around 40 emails per day

Again there might be other communication channels (such as phone, chat, video conference etc..) and tools (such as Microsoft teams, zoom, slack etc) that might be used with in the organisations which increases the amount of data generated all the time. In essence we need to treat data as the new currency which needs to be tamed in order to get value out.

Breakdown of the different types of Data

Types of Data

Generally data can be of different types

  • Transactional Data : data described as an internal or external event or transaction that takes place as an organisation conducts its business
  • Master Data : data describing people, places, and things that are involved in an organisation’s business
  • Reference Data : data are sets of values or classification schemas that are referred to by systems, applications, data stores, processes, and reports, as well as by transactional and master records.
  • Metadata : literally means “data about data.” Metadata label, describe, or characterise other data and make it easier to retrieve, interpret, or use information.
  • Historic Data : data containing significant facts, as of a certain point in time, that should not be altered except to correct an error.
  • Temporary Data : data kept in memory to speed up processing. They are not viewed by humans and are used for technical purposes
  • Reporting Data : aggregated data compile for the purpose of analytic and reporting

Data dimensions that needs to be tamed

There 5 key dimensions that needs to be understood including

  • Volume : Volume defines the ‘amount’ of data that is produced.
  • Velocity : Velocity refers to the speed at which the data is generated, collected and analysed. Data continuously flows through multiple channels such as computer systems, networks, social media, mobile phones etc.
  • Variety : Data sources may involve external sources as well as internal business units including smartphones, IoT devices, social networking etc..
  • Veracity : Veracity is more commonly known as validity and is the assurance of quality or credibility of the collected data.
  • Value : Data which can be harnessed by which business insights are generated add ‘value’ to the company.
Data is the new currency

Solution for end-to-end data management

An end-to-end data management solution will help you to get control of your data. This fundamentally changes how companies harness data in the long run allowing business intelligence teams to generate valuable insights. The solutions shown below only addresses technical solution of how a data management solution should work.

5 Pillars of DataOps

Conclusion

Our 5 step framework help you to implement your data management solution. The solution itself will only solve technical aspect of your data currency problem. Organisations should adopt a data-driven culture by implementing Data Governance, Data Management and Data Insights across the teams who are responsible for managing the data. This will ensure organisations achieve long-term and sustainable benefits, taking your further to next level like machine learning, data science etc.


At ACS we have experts who could help your organisation generate valuable insights of your data. Complete the form for a free consultation and we shall be in touch to understand your specific requirements.

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