Modern Data Platforms for Sustainable Data Driven Transformation

Modern Data Platforms for Sustainable Data Driven Transformation

Most probably nowadays people are hearing terminologies about data, analytics, data driven, modern data platforms, data warehousing, data lakes.?In this article we will talk about these concepts

Modern Data Management

The very beginning story is that the data is the Value of any digital transformation, the fact is that we define Data as the fuel for the Digital transformation vehicle. As a successful digital transformation should be a Data Driven.

Organization will continue to adopt technology and develop more internal applications and digital products trying to reach the best CX however viewing insights and performing data analysis for different business domains is a must to monitor and measure business profitability. These data analytics in terms of visualizations or analytical reports must be managed in a proper way.

Modernizing the data management capabilities, digitizing business by leveraging data & analytics relying on technology.

The data management process is about managing, collecting, storing, processing and protecting data to optimize operations and assist decision making.

And with the data management evolution and modernizing more the data management processes, helping streamline, fasting more, improving more the traditional capabilities of the organization. Enabling businesses for better fastest decisions addressing the current challenges and solving limitations adhere in the tradition data management.

Modern Data Management can have the following features:

  1. ?Centralization: No more data silos, no duplication, SSOT an organization Goal?
  2. User Centric: Business Users themselves, Data Owners, Stewards to change the way they treat data
  3. Cloud Based: Rely on Cloud computing, if no Public Cloud then Private
  4. Governed: Defining a DG framework, data protection and retention, comply regulations e.g. GDPR?
  5. Flexibility: Adopting changing in processes
  6. Cost Optimization: Maximize Business Value, increase operation efficiency, from customer centric to profitability?

You Should consider start by an Effective?data strategy- Why Not?

Data Strategy should be reflected to organization Strategy. A Data Strategy is a main pillar in success of Digital Transformation Strategy.

It should have Goals & Objectives with good Data Architecture and modern Data Management capabilities should be existing in the organization

Without proper Data Governance it is hard to develop and implement it an effective Data Strategy or modernize your data management

To Enable a Robust and comprehensive Data Strategy, start from the Business Strategy to focus on the organization objectives, initiatives for the business revolution and their KPIs.

Define the Organization Objectives & Goals, Business Drivers for digitization business to let the Organizational Drives the Data Value to fast the Organization transformation

In an Effective Data Strategy there must exists Objectives & Principles, Strategy Vision & Mission cascaded to all Human Resources

Plan a Data Strategy Roadmap in an agile approached to define all your sources, initiate the Data Lab, preparing and collecting your data into a Data Lake, performing modeling your information data model enabling the data warehousing process, and giving the end users all the capabilities of analytics, self-services, with a Data Governance Framework

We always say that it will be very hard to implement an effective data strategy and a modern data management without a proper Data Governance Framework.

Data Culture

Assess the organization Data Management Capabilities, by improving the data awareness for technology and technical tools, so it is important to assess the data management and integration tools. So design your solution to be Data as a Service.

Data Culture and Data Literacy must be a part from the data strategy in order to reach the data driven culture and let the whole organization speak the same language and treat Data as an Asset.

As a part from your DG framework you can start with People: Roles and Organizational Structure as who uses which application, who have access to what datasets, with having business glossaries and data dictionaries to understand well each element of attributes under these data sets.

The strategy of having the distributed data teams across different sectors that can benefits from the technology and the visualization tools that is the Self Services capabilities which is very important to be included in your services delivery to your business.

Data Ops is also important aspect has to be taken into consideration in order to automate more your data pipelines in terms of monitoring, maintenance and support.

At the End your goal and objective must be the Data Value by generating changing analytics empowering Machine Learning and AI, monetizing your data and transforming with data to a data driven culture with democratizing data as a fuel to data driven decision making.

Data Evolution

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Usually such transformations start from low understanding of data importance, rarely using data for decision making as we call this stage Data disengagement.

Then by introducing new technology and proper data architecture, people and processes optimization where it becomes the enablement phase of Data.

Then Differentiating and competing on data and analytics; we’re a data driven organization. Collaborating through data. Data is an asset.

Data Warehouse VS Data Lake VS Data Lakehoues

Data Warehousing before it was designed and implemented on a data base now by technology evolution and adoption is built on a Big Data platform, So the data warehouse is a process regardless technology.

-???????Traditional Data Warehouse

The very famous traditional data warehouse was designed to be implemented on a normal database RDBMS, extract all the data sources of different structure data into a single source of truth to a DWH architecture model whether a dimensional model start schema or snowflake schema or other satellite models. A then you can have some reporting, data mining, OLAP, operational data store but the challenges that was struggling of computing power and performance. It was also working on small portion of data the structure data only.

?-???????Modernized Data Warehouse

Then it was developed to some modernize data warehouses approaches like to build Data Lakes beside the old traditional data warehouse in order to start ingest and extract more type of data may their log files, the social media, sensors, any semi structures or un structure types of data to start enable some sentiment analysis, advanced analytics use cases fastest than before leveraging also streaming analysis and processing.

But it didn’t stopped at this architecture it is now going into more modernized data platforms.

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Modern Data Platform

One unified data platform built on Big Data Platform has a modern data architecture includes Analytics and AI as Services with complete Data Governance model

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Modern?data?platform unlock your data's potential by turning?it into actionable insights?and artificial intelligence (AI).

We get / extract all sources into Single Source of Truth Rely on Technology

Build Data Lake with a Proper Data Architecture then build an Information Model

Enabling AI / ML with Self Services

Ensure Data Governance meet the following

-????????Data Linage

????????Data Quality

????????Data Security

????????Data Catalogue

????????Business Glossaries

However, it becomes by the advanced technology evolution to Modern Data Lakehouse when Bill Inmon and Databricks announced the new book The Data Lakehouse end of 2021.

#datamanagement #datastrategy #moderndataplatform #bigdata #moderndatamanagement #datawarehousing #dataculture #datawarehouse #datalake #datalakehouse

Islam Mokhtar

Chief Executive Officer at Frontiers LLC

2 年

Awesome keep sharing

Mohamed GadAllah

Sr.Data Engineer Consultant at Devoteam | CDMP | Data Quality & Data Governance | ETL | BI

2 年

Great document, keep sharing

George Hany

NBE Banker | Treasury Back Office Specialist | CFA Investment? | Economics Grad - October 6 University | Former Trainee At 15 Financial Institutions | Enactus World Cup 2021 Champion | Finance Educational Background |

2 年

Thanks for sharing

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