How to Improve Your Big Data Management

Improve Your Company’s Big Data Management for Increased ROI

Over 2.5 quintillion bytes of data is created on the internet every day. Even the data that matters to your business is usually unstructured and disorganized, with lots of duplicates and inaccuracies. The difficulty of processing all this information makes it extremely difficult to make informed decisions.

With the advent of big data processing, organizations can translate that data into knowledge that improves decision-making, performance and business processes.

This hasn’t gone unnoticed: A 2020 report by MicroStrategy found that around 94% of companies believe that digital transformation centered around data analytics applications will be essential to their business growth.

The ever-growing field of big data has presented both opportunities and challenges for businesses across a wide range of industries. While large organizations have always collected massive amounts of data, the ability to store and analyze this data has increased exponentially in recent years.

However, Forrester reports suggest< that between 60 and 73 percent of all data within a given enterprise is never used for analytical purposes, meaning that most organizations are missing out on valuable insights.

However, according to Sigma Computing, 39 percent of business domain experts aren’t sure what being data-driven means, while 76 percent of data experts said they were spending half of their time preparing ad-hoc reports for business management teams.

It’s not enough to just have big data – businesses need a management plan to handle it.

What is Big Data?

Big data is simply too much data for traditional systems to handle. It’s often unstructured and usually doesn’t fit neatly into databases. This can make storing, managing and analyzing it using traditional methods difficult.

By processing big data, retailers can get insights into their entire supply chain. Marketers can effectively generate customer profiles and touchpoints for hyper-personalized messaging. Manufacturers can monitor and manage the performance of different components and the overall infrastructure in their facilities.

The Five Vs of Big Data

Big data is usually characterized by the 5Vs – volume, velocity, variety, value and veracity.

  • Volume: It comes in huge quantities.
  • Velocity: It shows up very quickly, and gets even faster over time.
  • Variety: It comes in a wide variety of forms, often unstructured.
  • Value: It can be valuable…if you can process it.
  • Veracity: Its value depends on whether you can trust it.

Benefits of a Big Data Management Plan

Forbes reported that 95 percent of businesses say they struggle to manage unstructured data. Big data management involves understanding the 5Vs of big data and using the right tools, technologies, people and processes to manage it effectively.

However, there are some major benefits to handling big data well. McKinsey reports that data-driven organizations with insights about customers are 23 times more likely to outperform their competitors in terms of new customer acquisition. They’re also nine times more likely to maintain the customers they gain.

Return on investment shows roughly the same picture: Companies making intensive use of a customer analytics solution are 2.6 times more likely than those that aren’t to have a significantly higher ROI than their competitors.

Here are just a few of the benefits of managing big data:

  • Improved decision-making by drawing insights from data that was previously too overwhelming to process.
  • Increased operational efficiency due to streamlined processes.
  • Improved customer experience due to better understanding of customer needs and preferences.
  • Increased revenue thanks to the identification of potential new revenue streams.
  • Reduced cost due to the ability to identify waste and inefficiency.

Challenges in Managing Big Data

According to a CIO report, 80-90 percent of data we generate is unstructured. 

Because of its size, complexity, and variety of sources, unstructured data poses a number of challenges for the organizations that need to manage it. Here are some of the biggest ones: 

Data Quality

Because raw data is often unstructured and drawn from multiple sources, it’s difficult to ensure it’s consistent and accurate. 

Data quality issues can arise at every stage of the data lifecycle, from data collection and storage to analysis and decision-making. Dirty data can lead to inaccurate insights and decisions and can even result in financial losses. 

Data Integration

Data integration is the process of combining data from multiple sources into a single, coherent view. It’s difficult to combine into a single format for analysis and decision-making because of its volume, velocity and variety. 

Data Storage and Processing

47 percent of enterprises cite data growth as one of their top three challenges. 

The processing demands of high-volume, high-velocity data can strain data storage and processing limitations. Even with the proper big data architecture, it’s costly to store all of this data; even more so if it needs to be kept for a long period of time.

Privacy and Security

According to Capgemini, data security concerns are a major hurdle, second after budget constraints, to effectively turn big data into a profitable asset. That’s because the large volume of data can make it difficult to keep track of who has access to it. 

Data governance

In a Mulesoft report, 54 percent of organizations cited security and governance as their biggest challenge. 

When you’re watching over vast amounts of data that all looks and acts differently, it’s very difficult to ensure sensitive information is properly used and stays in the right hands.

Scalability and Costs

The same Capgemini report revealed that the biggest reason organizations are failing to invest in big data is a lack of IT budget. Mulesoft’s report also showed organizations are spending over $3.6 million on average on custom integration labor. 

As there is more strain on data storage and processing, the costs to manage and scale the systems also become equally challenging. 

How to Improve Big Data Management

Big data has led to a greater understanding of customer behavior, trends, and preferences. However, it has also created new challenges in terms of managing and protecting this sensitive information.

Here are just a few potential ways to make it easier:

1. Understand Data and Business Goals

The first step in improving big data management is to understand what data is available and how it can drive business goals. This is critical to ensure that the data will be used to drive the organization’s objectives, not just being collected for the sake of it.

Once management teams understand this, they can determine which tools and processes are necessary. 

2. Implement Data Quality Control Processes

Organizations should ensure that their data is of high quality through data cleansing, profiling and validation techniques. 

Organizations should also put in place processes to monitor the quality of their data on an ongoing basis. This will help them quickly identify and fix any problems with the data.

3. Invest in the Right Tools and Technologies

Managing big data requires the right tools for the job. Some of the most popular big data management tools include: 

  • Distributed processing frameworks Hadoop and Spark
  • Cluster management software
  • NoSQL databases
  • SQL query engines
  • Data lake and data warehouse platforms
  • Cloud object storage services
  • Stream processing engines

Organizations should also consider investing in cloud-based master data management (MDM) solutions with artificial intelligence capabilities, as they are scalable and cost-effective.

4. Develop a data governance framework

Organizations should put in place a data governance framework to manage their data. The data governance framework should include policies and procedures for managing big data. It should also define who is responsible for managing the data and how they should do it. 

These controls are extremely important for data security, privacy and compliance purposes, as they keep track of the data trail and protect the data from unauthorized access and use.

How MDM Solutions Improve Big Data Management

An MDM solution is a useful tool for big data management. They use machine learning to help organizations cleanse and validate unstructured data and consolidate it into a single source of truth.

This helps solve several problems involving data quality, governance, analytics, costs and more. 

MDMs can support big data by:

  • Optimizing data access – MDM provides a centralized, accessible of an organization’s data. This makes it easy for both technical and business users to find what they need. 
  • Cross-domain relationships – MDM platforms make it possible to integrate and share data of various types from multiple domains. 
  • Real-time entity identification – Organizations can use MDM to create a reference database of known entities, such as customer or product records, to offer rapid, easy-to-use data access. 
  • Improving data quality – MDM enriches data by removing duplicates and flagging missing information,  reducing the amount of data to process. It also groups similar records together so they can be managed as a unit.
  • Improved governance and privacy –MDM makes data consistent and dynamically masks sensitive data, making it easier to use without violating data privacy policies.
  • Integration with cloud providers – MDM allows you to leverage the scalability, flexibility, reliability and security of the data living on different cloud providers.

Agile Master Data Management from Reltio

Reltio’s agile master data management solution helps organizations gain higher-quality, contextual data that powers faster, smarter and more profitable business decisions and operations. Learn how we can help activate your big data at the quality, scale, flexibility and speed demanded by modern business practices. 

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