How to Get Started with the Modern Data Management Approach
By: Kavika Roy
Tags: Big Data Analytics, Big Data, Data Management, Business Intelligence, Data Analytics

Modern Data Management Approach: Things to Know

For an enterprise to get started with the modern database management approach, it needs to set up the basics and has a clear idea of the following-

1. Data Architecture Blueprinting

The data management architecture used by many enterprises is a traditional model that’s decades old. Instead of replacing the entire setup, businesses have been adding new tools to the existing setup. This has created a complex mix of old and new that doesn’t deliver the kind of results an enterprise needs.

The traditional architecture doesn’t support big data analytics. And without big data technologies, SMEs cannot get predictive insights to make better decisions. Modern data architecture supports vast volumes of data and can deliver the results while being a cost-effective solution. It can handle diverse data types and process them with ease.

It empowers the enterprises with reliable and accurate insights to make the right decisions. The first step to follow the modern data management approach is to understand the data storage and processing requirements. Today’s data storage needs to accept unstructured, semi-structured, and structured data and allow diverse data analytical tools to be used on the data.

It’s important to know if the enterprise needs to invest in a data warehouse or opt for data lakes. The overall data architecture needs to be flexible, scalable, accept vast volumes of data, offer low latency, and be a cost-effective solution for the business.

2. Manage Data Quality

If the traditional data architecture methods don’t work with big data, it’s natural that they won’t help with managing big data quality either. That’s because the traditional systems have been designed for structured data. Big data contains all and formats with lots of real-time data, none of which is organized. While the data cleaning processes used in business intelligence are effective, they still don’t work the same for big data.

In fact, it’s not easy to define quality for big data. How do we say what’s good and what’s not? The level of quality required in data is determined by the purpose for which it is used. For example, financial data analytics needs higher accuracy. Marketing analytics relies on behavioral data. Predictive analytics uses data on the ends of the spectrum to make future predictions. Enterprises need to assign their own parameters to define and manage data quality.

3. Data Modeling Practices

Modeling data from traditional warehouses is based on creating structures that work with relational databases. But the same doesn’t work for big data. There is no need to follow the pattern of conceptual modeling to logical design to developing a physical database.

Modern database management services work on the attributes and relationships between various aspects of data. Since data is already present in its different formats and structures, the focus here is on identifying the relationship between the entities and understanding how one affects the other. The modern NoSQL database supports many-to-many relationships, attributes with multiple values, embedded arrays, and a lot more than relational databases can handle.

4. Curating and Cataloguing Data

One of the major differences between traditional systems and modern systems is the source of data. Previously, data was generated by the business and stored within the enterprise. But big data comes from multiple sources at a greater velocity. It has more variety than past data. Datasets need to be a combination of internal and external data.

Data curation is the process of collecting data and making it available for data analysts and data scientists to derive insights. Cataloging is an integral part of data curation that enables enterprises to identify different data sets using data tags and meta tags. Without this step, setting up a modern data management system is not complete.

5. Identify a Team Leader and Business Capabilities

Do you know why data-based projects don’t deliver expected results? Not having an efficient leader and/ or not having the necessary business capabilities are the most common reasons for big data analytics to fail.

It is essential to have a data science team that works in sync with the management and other teams. The teams should share a common goal and have a comprehensive strategy involving people, processes, technology, and data.

Enterprises should start by working with the stakeholders to identify and make a list of business capabilities that rely on data. Itemizing these capabilities will give everyone a clear picture of what needs to be done to follow the modern data management approach. These can belong to any industry and can either be small enterprises or multinational organizations.

6. Select Data Principles for People and Processes

We’ve mentioned people and processes in the previous point. Let’s focus a little more on these aspects. When we think about it, management needs to make decisions about investing in data-driven approaches to facilitate better decision-making. To put it simply, unless we decide to empower our decision-makers, we cannot guarantee that they will make the right decisions. And it’s also important to convey the worth of the decision to stakeholders and make them understand. The whole thing sounds complex but is quite simple.

Data principles are a set of predefined rules that help enterprises align their decisions with the short-term and long-term goals of the business. The decision-makers will be equipped to do the right thing and convey the same to the stakeholders who have meager knowledge about the backend working of the data-driven approach.

7. Risk Mitigation and Data Security

Establishing a reliable data management system where data is collected, cleaned, validated, processed, and protected is essential to provide the necessary infrastructure to data analysts. Amazon Web Service (AWS) is a data management services example that offers a flexible cloud data management setup with access to countless tools. Microsoft Azure and Google Cloud are other famous cloud-based providers for data management tools.

The modern data management approach helps businesses create a balance between being ready to grab market opportunities and mitigating risk. Risk isn’t limited to market conditions or customer behavior only. It is also related to data security. Even a small mistake could lead to losses of millions. The last thing any enterprise would want is to end up in the news for being a victim of a data breach.

It is highly crucial to create and establish a comprehensive strategy for data security and risk mitigation before choosing the tools and service providers for modern database management. It’s necessary to know how to store, manage, and protect big data to gain reliable insights.