Demystifying the Data Deluge

Apr 04, 2023
Digital Transformation | 6 min READ
Every organization knows that data is a lens to the past and the key to the future. In the simpler days gone by, organizations’ questions revolved only around newer and more efficient ways of sourcing the data to be processed. However, the quest to become data-centric in the age of advanced analytics and ever-evolving AI and ML algorithms has left enterprises wanting. Facing a deluge of data from the internet, assets, internal processes, and myriad other sources, organizations are struggling to identify and store the right data in a bid to systematically derive value from it.
Deepak Gupta
Deepak Gupta

Former AVP, Practice Head

Data & Analytics


Rishu Sharma
Rishu Sharma

Practice Director

Digital Evangelist and Storyteller, Digital BU


Sources of Data and the Need for Selectivity
Data, often called 'the new oil,' can be structured, semi-structured, or unstructured and can be extracted from assets, processes, customers, employees, workplaces, warehouses, and a host of external sources. Hoarding data without reason not only increases costs of storage, processing and analysis but also increases the chances of confusion, misinterpretation and misrepresentation of irrelevant data and outdated insights. Hence, organizations must make an effort towards understanding the data they truly need.
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For instance, an organization in the manufacturing space could extract valuable insights from the data generated by plants and industrial assets. However, data from social media or the internet may not be of much use to it unless it is related to weather forecasts or policy updates from the government and related websites. The bottom line is that each organization has to decide not just what data adds value and what does not but also how to store such data and for how long.
Data Storage
Structured data may be stored in data warehouses, while raw data is usually stored in data lakes that support multiple data types. Data storage is possible on the cloud, on-site, in hybrid models, and in Hadoop, MongoDB, RainStor, NoSQL databases, Object storage, etc.
Data security is paramount, and classification, encryption, firewalls, authentication methods, access management, and various other measures need to be in place to ensure that sensitive data is protected at all costs.
Mining, Analyzing, and Visualizing Data
The tools that organizations may use for data mining include Rapid Miner, SPSS Modeler, KNIME, and Orange. These tools help to extract the required data from warehouses. Organizations can harness tools such as Apache Hadoop, Apache Spark, SQL, Presto, Splunk Hunk, and others to analyze data and uncover insights.
Visualization and communication of these insights to end users constitute the next step. Tools that can be used for visualization include Tableau, PowerBI, Plotly, Python, and Qlik Sense.
Role of Data Governance and MDM in building a Data-centric Culture
Role of Data Governance and MDM in building a Data-centric Culture
Data Governance ensures efficient and consistent data usage across the organization. Strategic and efficient data governance helps organizations reap the benefits of data quality, consistency, reliability, security, transparency, and privacy. Policy-driven access management and data classification go a long way in preventing misuse of data and security issues.
Data Governance is essential to foster a data-centric culture since it helps inculcate and propagate data literacy. It also enhances cohesion between departments, people, and processes through a universal understanding of the data culture which allows the organization to reap benefits beyond data-driven visualizations, decision-making, and insights.
In parallel, Master Data Management (MDM) can also help consolidate and rationalize data from various sources in the organization, providing a 360-degree view of enterprise data and a single source of truth to ensure uniformity, accuracy, and usability. This leads to consistent and efficient strategy and operations across the enterprise. With MDM in place, organizations are a step closer to inculcating a data-centric culture as people gain access to trustworthy, high quality data and are empowered to make better decisions that in turn enhance business outcomes.
Five Steps toward a Robust Data Management Strategy
  • Identify business objectives and the right sources of data
    The first step in choosing the right data is to identify the business objectives and outcomes to be fulfilled. Simultaneously, data siloes must be broken down to prevent data duplication. Organizations must make sense of what data is being collected and why, as storing, managing, and analyzing irrelevant data will only add to the costs. Factors such as privacy and security must be considered.
  • Focus on specific use cases that further the progress toward objectives
    The next step is to identify well-defined use cases that would help the enterprise achieve its objectives. It is advisable to avoid complex use cases with factors beyond control and those which do not add significant value. As organizations work on the selected use cases, they are also likely to identify irrelevant datasets that can be ignored, leading to better cost optimization.
  • Data Governance and defined zones - Landing zone, Production zone, Working zone, Sensitive data zone
    The next step is to define and prepare the infrastructure where data can be processed and analyzed. This can be done in a data lake on a single cloud system, a multi-cloud system, or even on a hybrid system of cloud and on-premises. Creating a data lake can be the step that leads to the democratization of data in the organization. Next, the zones in the data lake should be defined. Ingestion of new data happens in the landing zone. In the production zone, data is cleaned, processed and stored. In the working zone, the data scientists and other users can explore and innovate with the available data. A sensitive zone, which may not be applicable to all organizations, usually involves a zone with restricted access to sensitive data. Some important aspects to be considered in this step are access management, data file formats and sizes, whether to have single or multiple data lakes, and whether they should be global or regional.
  • Compliance with regulations
    It is necessary for organizations to comply with ever-evolving industry standards, laws, and regulations revolving around the usage, storage, and management of data. It is crucial that compliance is built-in at every level and in every data governance policy applied across the organization.
  • Training and equipping employees with the right tools
    The final and crucial step is the organizational change required to bring people, processes, and technologies together to implement and continuously improve the strategy. This involves getting a buy-in from all the stakeholders, training and proliferating the vision and objectives of the data management strategy, and taking steady steps toward becoming a data-centric organization.
Birlasoft can be your partner in your transformation into a data-first organization.
Data Governance for a large Pharma Manufacturer
A large pharmaceutical manufacturer was keen to transform and accelerate the decision-making process and further their ability to search, add and grow data resources and fill data gaps across R&D. In due course, they wanted to build a Data/API Marketplace to advance R&D decisions.
During the course of the engagement, Birlasoft designed the Data Governance Organization structure, curated Tag data, configured data lineage between TAMR, AWS Data lake, and consuming systems, and also helped to implement the R&D catalog in Alation and Collibra for data classification based on business needs such as biological insights, diagnosis, and disease expression.
With Birlasoft’s expertise, the client experienced improved visibility and insights for intelligent decision-making. There was a marked increase in the adoption of available data assets for R&D decision-making and a reduction in the drug discovery cycle time.
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