Solving the Pain Points of Big Data Management

Every business aims to deliver products and services quickly and efficiently based upon customer wants and needs. Today, much of that speed and efficiency relies on insights driven by big data. Yet big data management often serves as a stumbling block, because many businesses continue to struggle with how to best capture and analyze their data.

Unorganized data presents another roadblock. Without proper organization, it’s nearly impossible to extract meaningful insights.

The Four Major Pain Points in Big Data Management

Before a business can effectively collect the right data in real-time, analyze it, and then use it to improve business outcomes, it must first be able to avoid or solve the common issues inherent in data management. Here are four of the top pain points that businesses are experiencing with data management—and how to overcome them:

1. Not allowing business needs to drive data and cloud strategy

With big data, often the cart is put before the horse. For example, some businesses tout “cloud first” as a solution. But while cloud plays a significant role in infrastructure, storage, data capture, and data processing in today’s business environment, each organization needs to clearly define its business needs first.

Knowing what business outcomes you’re aiming for should drive your data strategy—which should then drive your cloud strategy.
2. Lacking agility to respond to customer needs

Data can reveal many things about your customers, including what they buy, what they think, and what they respond to. Your organization needs to be agile enough to extract data quickly, even in real time, and respond just as quickly.

An agile data operation allows an organization to reduce time to market, attract new customers, and find new paths to customer interaction. Many businesses find it hard to harness this need for speed in a useful way.
3. Failing to leverage the connected world

Sensors, IoT devices, mobile devices, cities, cars—the digital world is increasingly connected and interconnected. This connection is changing the types and sources of data businesses are receiving. The enterprise is challenged to collect vast amounts of data, govern it, secure it, and enforce the regulations affecting it.

Complicating things further is the need to know where the data is located and who has touched it, interacted with it, or changed it. The collection, management, and governance of data confounds many enterprises and presents a hindrance to using this data treasure trove to its fullest.
4. Not having flexible infrastructure to accommodate big data management

Volume, variety, velocity—you’re no doubt familiar with the three big V’s of data, but understanding the infrastructure needed to manage them may be less clear. You require an agile, flexible infrastructure that can meet today’s business needs and scale to match how those needs will change tomorrow.

Most organizations address this need in the cloud, yet that sometimes offers its own pain points. Predicting costs, avoiding vendor lock-in, and spinning down unused resources are challenges most organizations try to avoid.

The Competitive Advantage of Overcoming Data Management Pain Points

Enterprises recognize that they need a new approach to data management architecture. Despite all the data available, much of it resides in silos.

If data can’t be used by everyone who needs it, then it does little good. If analytics processes can’t reach all data sources, then you only get a partial picture of your customers and their needs. If you can’t extract data insights in real time or near-real time, then all you have is a snapshot of the past that cannot accurately predict your future. If you don’t have any way to forecast your cloud spending for data processing, compute, and storage, then you’re writing a blank check to cloud vendors without much to show for it.

It becomes an expensive waste of resources and opportunity costs to allow these pain points to continue.
The Enterprise Data Cloud Approach

Overcoming these challenges requires a new architectural approach to data management, like an enterprise data cloud. This type of approach addresses the pain points outlined above by offering agility, flexibility, connection to the connected world, and easy alignment with business strategy. Enterprise data cloud also provides a single platform in which to view your data sets, consistently manage data governance and security, and finally get a handle on your cloud spend.

Enterprise data cloud allows the application of many analytic disciplines against data—whether it resides in a hybrid or multi-cloud environment or on premise. With enterprise data cloud, enterprises can process and stream real-time data from edge endpoints and help businesses predict outcomes or apply machine learning. In the next few weeks, we’ll be diving deeper into how an enterprise data cloud strategy can transform your approach to data and give your enterprise a competitive edge, so stay tuned for more.