IJBTT

The Importance of Data and Analytics Provenance and Governance in the Realm of Datafication
	© 2022 by IJCTT Journal
	Volume-70 Issue-1
	Year of Publication : 2022
	Authors : Prashant Tyagi, Sharada Devi P.P
	DOI :  10.14445/22312803/IJCTT-V70I1P106

How to Cite?

Prashant Tyagi, Sharada Devi P.P, "The Importance of Data and Analytics Provenance and Governance in the Realm of Datafication," International Journal of Computer Trends and Technology, vol. 70, no. 1, pp. 28-33, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I1P106

Abstract
This long paper discusses the importance of having a strong Data Governance program in place for organizations to trust their data so that this governed data becomes a key asset and can feed into various reports that businesses rely on heavily to drive positive results for their enterprise. In the world of digitization, where Industries, Organizations and Businesses are increasingly embracing digital-first business strategy, they are beginning to reinvent and redefine how they are conducting business fundamentally by adopting and implementing newer technologies to help them succeed, maintain their strategic position in the market and gain competitive advantage in an ever-changing business climate [1]. In the process of dramatic digitalization, globally spread-out communication networks, highly available, highly scalable computing capacity in the cloud and hybrid-cloud environment datacenters have given the capability for companies and humans to share information seamlessly to empower them with the ability they need to make faster and quality time-saving decisions. So, what is Datafication? Datafication, on the other hand, can be thought of as having a common theme with the Operational Technology (OT), which deals with more of unembedding the knowledge tied to the physical objects by detaching them for the data associated with them[2]. Having exposure to such a plethora of data, it is then very critical to trigger the discussion and the thought process around where to draw boundaries between corporate and social and governmental responsibilities and the public and private lives of people. Having said that, the provenance of the things or the data lineage matters for the data that powers the analytics and drives critical business decisions. The work in this paper discusses the importance of having an end-to-end data lineage, data provenance and data governance program in place all along the information value chain to help analyze and determine whether such information/data is “fit for purpose” as it gets reconstructed and transformed into an information product [2].

Keywords
Data governance, Data provenance, Data lineage, Digitization, Digitalization, Datafication, Operational Technology (OT), Information Technology (IT), Analytics, Data, Cloud, Hybrid-Cloud, Digital, Transformations, Initiatives, Security, Data quality.

Reference

[1] Prashant Tyagi, Convergence of IT and OT – Cybersecurity Related Challenges and Best Practices, International Journal of Computer Trends and Technology 69(2) (2021) 85-92.
[2] The impact of datafication on strategic landscapes by Ericsson
[3] Collibra.com https://www.collibra.com/wp-content/uploads/Ebook-DataLineage-20200113.pdf
[4] Topper Tips Unconevtional https://toppertips-bx67a.ondigitalocean.app/data-lineage-vs-data-provenance/
[5] Prashant Tyagi and Sharada. Devi. P.P, A Functional View of Hybrid Cloud Environment-Use Cases and Best Practices, SAP Publications http://article.sapub.org/10.5923.j.computer.20211101.02.html.
[6] Gartner Report Predicts 2020, Analytics and Business Intelligence Strategy
[7] Transforming Data With Intelligence, Avoid Mistakes of the Past on ModernCloudDataManagement, https://tdwi.org/webcasts/2020/05/adv-all-avoid-past-mistakes-use-modern-cloud-data-management-to-deliver-faster-value.aspx
[8] Datasheet from Qlik, Data Integration, Enabling Analytics with Trusted, Business Ready DataQlik Catalog
[9] Informatica Whitepaper, FiveKeys to Optimize Your Data Lake with DataGovernance, https://www.informatica.com/lp/five-keys-to-optimize-your-data-lake-with-data-governance_3597.html
[10] Prashant Tyagi Diagnostic, Descriptive, predictive and Prescriptive Analytics Using Geospatial 69(1) (2021) 18-22
[11] Driving Data Governance with Data Quality https://www.talend.com/resources/definitive-guide-data-governance/