Data Governance Strategy to Accelerate Digital Transformation

Data Governance

Data is a new boom for digital transformation. In this era data is used everywhere. It helps to improve the decision-making process, get deep insights, and unlock the unavoidable hurdles. A digital transformation that focuses on data and analytics can enable technology and processes that will help gather and analyze that data so that you can meet customer expectations even as they evolve.

Data governance is the process of managing the availability, usability, integrity, and security of data within enterprise systems. This is “governed” by internal data standards and policies that govern data use, and effective controls ensure that data is trusted, consistent, and not misused. The value of big data in digital transformation stems from the ability of organizations to combine the two in their efforts to digitize and automate their business processes. This enables organizations to become more efficient and innovative, creating new business models through digitization and automation.

BDB approach to Data Governance

Data Governance is a set of principles and practices that ensure high quality through the complete lifecycle of your data.

Data Governance involves control and organization of data in a way that gives peace of mind to executives and business users.

A BDB Data Governance solution takes care of these concerns, and that coupled with effective data governance practices enables an organization to develop greater confidence in its data, which is a prerequisite to making data-driven business decisions.

For each governance activity, data governance tools execute policies such as

• Extract, transform, and load

• Data quality maintenance

• Meta Data Management (MDM)

• Life-cycle management

These tools also monitor security and metadata repositories

Features of Data Governance

Data governance aims to improve the processes involved in collecting, storing, and protecting an organization's data. A data governance plan provides a roadmap for organizations to realize positive returns from data capabilities while improving risk management. Here, we'll take a closer look at the core capabilities of an effective data governance plan.

Improve Quality of Data

BDB Data Preparation allows you to interact with your data in Excel-like interface. It lets you perceive a quality profile of your data, detect anomalous records (rows with invalid or empty values) and remove them in a few clicks. The user can get intelligent data transformation suggestions based on data type and quality.

Data Management

If Data is essential to Data Analytics, it needs a Data Center in the Platform to fetch data from the remote storage, process, or old it to fit in a meta-structure as per the need for analytics. BDB Platform has 60+ built-in connectors for connecting to real-time and batch sources from RDBMS, Big Data, Flat Files, APIs, etc. Data engineers can connect to these sources and create Data Sets and Data Stores. BDB Data Store allows you to import your data from external sources into the Platform using SQL queries or API calls. These data stores can be used further for data exploration through the slice-and-dice method in the visualization layer of the Platform.

Data Loss protection

The Data Loss Protection component helps in protecting the data and ensures that the accumulated data by an organization is not lost, misused, or exposed to unauthorized users by the end-users. DLP aims to boost information security and ensure that customer data is protected from data breaches. This entire process is worked out by the prevention of moving the key outside of the organization network. The DLP component is provided inside the Data Pipeline module where the user can create the workflow and use all the DLP algorithms like masking, Hashing, Redaction, etc.

Importance of Data Governance

Data governance is important to give meaning to your organization's data.. It adds Trust and understanding to an organization’s data through stewardship and a robust business glossary, thus accelerating digital transformation across the organization. There are main benefits that say why data governance is important:

Data Governance Saves Money

Simply put, data governance improves efficiency. Duplicate accounts double the effort. Data governance reduces database errors, provides a robust database to work with your business, and saves valuable time spent correcting existing data. Saving time is saving money.

Bad Data Governance is Risky

Lack of effective data governance will impact on the security of the data of an organization. Bad data and badly structured data shoot a security risk that if you have dirty, unstructured data clogging your database. How can you quickly tell when something goes wrong and how can you efficiently monitor data is at risk?

Good Data Governance Provides Clarity

Take a second to imagine what the assurance of perfect data would to your business. Effective data governance ensures that data is generally clean, standardized, and accurate. The impact remains on the company.

BDB Decision Platform is a modern enterprise end to end analytics platform that enables from data ingestion, data transformation, Data Science AI/ML and self-service analytics at scale through governance. Security is the first and most critical part of a data and content governance strategy. BDB Platform provides the comprehensive features and deep integration to address all aspects of enterprise security. BDB Platform helps organizations promote trusted data sources so all users have access to the right data to make the right decisions quickly. ​

Ingestion components allow the users to ingest data in the pipeline from outside the pipeline. The users need to do Data Profiling to figure out what data they want to extract using various Ingestion APIs based on their structure. Data Ingestion is the first layer in the Big Data Architecture — this is the layer that is responsible for collecting data from various data sources—IoT devices, data lakes, databases—into a target data warehouse. This is a critical point in the process — because at this stage the size and complexity of the data can be understood, which will affect the architecture or every decision we make down the road. The Data Pipeline module provides different ingestion components like Twitter, SFTP Monitor, API, etc. Once we get the data using these ingestion components, it will feed as input to the pipeline. The Data Preparation module will detect the anomaly and correct and modify the ingested data.

Enrichment and preparation include the process of enriching, improving, or preparing raw data to make it analysis-ready. In many cases, no single data source can answer all the questions a user may have. Adding data from different sources adds valuable context. As you ingest raw data from various sources, you likely have data preparation processes in place to clean, combine, aggregate, and store your data. Data analysts can instantly detect anomalous records (rows with invalid or empty values) and purge the unwanted datasets in a few clicks using Machine-Learning based smart techniques and sampling. Identify errors and apply changes to any size of the dataset from any source. Export the analysis-ready data in minutes to the Data Pipeline module.

Data Science Workbench comes with Integrated Algorithms from R, Spark ML, Python, Keras + TensorFlow to create workflows and derive business insights. Custom Algorithms in R, Spark ML (Scala), and Python can be designed and utilized as and when required. Sentiment Analytics, Image, and Video analytics can also be performed using Deep Learning Workbench. Created models and publish them to the Data Pipeline.

Created Preparation and Data Science Models which are exported to the Data Pipeline will be integrated with pipeline components and execute the pipeline and processed data stored in the Data Lake. Once the Data Lake is created successfully will create dashboards on top of the data lake.

BDB’s signature visualization tool, Dashboard Designer is a complete package to create governed dashboards with prebuilt capabilities which makes it simple enough for business users to understand yet robust enough to accommodate custom scripting and visual requirements. High-level visualizations backed with accurate predictions, and real-time updates available at seamless speed provide precise insights to users to help them make informed business decisions.

AI based Anomaly Detection

There are 800 plus tables which are ingested in data lake via batch process; rule based anomaly detection has been set in place. BDB is interested in following to improve the data quality and alert mechanism: ​

• Dynamic alert system based on AI ​

• Statistical analysis data tables on fly with automated Alerts ​

Solution using Time series forecasting ​

• Model trains on time series data and predicts the values based on the input data.​

• Mean Absolute Error is derived from the actual values and predicted values.​

• An upper (Threshold 1) and a lower threshold (Threshold 2) are decided based on the Mean

  • Absolute Error and data points outside these threshold values are the anomalies in the data, which are indicated in ‘Red’.​

• Models to be used for Prediction:​

  • RNN – Neural networks​

  • Holt winters / exponential smoothening ​

  • ARIMAX/ GARCH

Solution using sample data ​

• Principal component analysis (PCA) to decompose many metric time series into a few representative bundles for scalability.

• ​Two principal components were computed which was able to capture all the variance in the data almost completely.​

• The principal components were plotted to graphically visualize the anomalies in the data.​

Data Quality Dashboard

Conclusion

Ultimately, data governance improves business decision-making by providing better, higher-quality data to management, leading to competitive advantage and increased revenue. Data governance provides enterprises with a plan to ensure that data is available, usable, consistent, and secure. This includes creating accountable processes to ensure data management is effective. This means the data is uncorrupted and available to anyone in your company.

BDB is a low code, hyper Automation, Data Analytics (AI/ML) platform that accelerates Data Ops &AI Ops for Enterprises on course to Digitization & Data Monetization.