How DataOps Can Solve the Big Data Problem

by Riversafe

The big data problem

As data becomes more available, inexpensive, and plentiful than ever before, it’s vital to re-examine how we interact and engage with data at an operational level. In recent years, the harsh consequences of poor-quality data have been realised, with issues ranging from slower productivity to loss of revenue, while the discourse surrounding data management best practices has seen a steady increase.

More recently, a new term has been integrated into this discussion; DataOps, and if businesses don’t get acquainted with it fast, they could risk being left behind for good.

Below, we examine the concepts of data management and ‘DataOps,’ analysing how these practices might just be the answer to the underlying issues of data storage and processing. From analysing the consequences of choosing not to implement DataOps to exploring what a successful implementation of DataOps looks like, read on to learn more about the latest advancement in data technology.

Three key challenges in data collection and storage

There are several key challenges with data management, from collection to storage, that businesses must navigate if they wish to implement a successful DataOps framework. Here are three of the most common challenges that organisations can expect to encounter:

1. Data Corruption

All data has the potential to be corrupted. Corrupted data can have disastrous consequences for organisations of any size. Reducing the possibility of corrupted data is an ongoing process, and new advancements in this area are released every year.

2. Establishing a long-standing data infrastructure

Once data has been collected, it needs a physical space to be stored. To cope with the large demand for infrastructure that accompanies big data, businesses must evaluate their current operations and consider alternative solutions. Cloud hosting and storage has become incredibly popular because of this challenge and is a viable option that businesses may want to consider.

3. Cost of hosting and storing data

Maintaining and hosting a centre for data storage is an expensive process. Businesses who rely on access to large sets of data need to consider a range of costs from initial set-up, to regular maintenance, to operating crews.

These challenges, when left unaddressed, may lead to poor data management – ultimately resulting in lower-quality data which may incorrectly inform market-leading decisions, among other consequences.

For businesses to make effective use of their collected data, they must consider two essential data strategies; data management, and DataOps.

What are data management and DataOps?

Data management and DataOps combines to revolutionise and transform how businesses interact with and utilise big data.

When correctly integrated, they can generate meaningful and future-proofed benefits, while eliminating unnecessary time and effort spent navigating poor-quality frameworks.

Data management

Data Management is the practice of organising, collating, and storing data efficiently and cost-effectively. In doing so, businesses may expect to reduce some of the negative consequences that accompany the collection and use of poor-data.

DataOps

DataOps aims to improve and build on this concept. DataOps is a data management framework that emphasises collaboration, communication, and productivity. In employing a successful DataOps framework, businesses may also be able to eliminate harmful data silos, increasing productivity and eliminating unnecessary costs.

Additionally, a successful DataOps framework is self-realising. No matter the scale of data, a DataOps framework is fully automated, ensuring that your system is future-proofed and eliminating the need for technical teams.

From emphasising user interaction to openly embracing change and advances in technology accessibility, there are a wide range of principles that inform a successful DataOps framework.

Why are data management and DataOps needed?

The primary goal of data management is to help aid organisations and businesses make improved market-leading decisions by optimising the processes in which the team interacts with their data.

DataOps takes this one step further by focusing on interactions surrounding big data. The larger the dataset becomes, the more measures there must be to handle and organise it, and with a successfully deployed DataOps framework, incredibly vast sets of data can be navigated throughout the entire business with ease.

This means that integrating DataOps practices make businesses more agile to data growth. It helps them to scale their data practices, incorporating bigger, faster, and more complex datasets, while maintaining seamless data management.

Without implementation, businesses can expect to face disruptive and destructive consequences.

Major Consequences of poor data management

Businesses that choose to ignore the option of DataOps put themselves at risk of falling behind their competition and losing potential streams of revenue and growth. Some of the most critical consequences of poor data management include.

  • Poor decision making

Data can directly influence market-leading decisions. If this data is low-quality and potentially misleading, businesses may end up investing time and resource into unprofitable areas.

  • Poor productivity

Not only does poor data management have a direct impact on critical decisions, but it also has an impact on the everyday operations of the business itself. Every single employee is affected by poor data management. It can significantly reduce efficiency, stall production, and slow down business operations overall.

  • Increased costs

Decisions that were informed by poor quality or incorrect data don’t only cause inconvenience; they can cost businesses a major less in revenue. Invested time and effort (that could have been delegated to more meaningful streams) based on poor-quality data will see little to no benefits or return, while facilitating unnecessary spending.

  • Production of data silos

A data silo is a set of data that’s accessible to one department, but otherwise unknown or inaccessible to the rest of a business or team. As a result, teams are often subject to miscommunication and confusion which can directly harm a collaborative and organisational workplace culture.

These consequences show how important it is to thoroughly assess your data management framework. The quicker you eliminate any inaccuracies or redundancies, such as harmful data silos, the less risk there is of falling victim to poor data.

By establishing an operational DataOps framework, businesses can expect to reduce, or fully eliminate, these challenges.

RELATED: The Ultimate Guide to Data Operations

What does a good DataOps framework look like?

To accomplish and demonstrate its principles, a good DataOps framework will complete four key objectives:

  1. Bring visibility and transparency to data stacks and operations.
  2. Improve the quality of a business’s data.
  3. Build trust, reliability, and authority of presented data.
  4. Reduce the time it takes to make data-driven decisions.

To implement a successful DataOps framework, businesses must improve and evaluate both the underlying technology used to process and collate data, as well as the infrastructure that holds and accounts for increasingly vast datasets.

In doing so, businesses can move towards a more automated, integrated, and easily accessible DataOps framework. A successful DataOps framework can provide and nurture a clear culture of communication and collaboration. Another follow-on effect is the elimination of data silos, ensuring an end to misinformation and frustration.

Join the DataOps revolution

For more of the latest insights in data operations and technology, why not visit our tech blog here. If you have a specific question and would like to get in touch with one of our specialists, contact us here via phone or email.

Riversafe

By Riversafe

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