Data is key for business success, but it keeps getting more challenging to manage it. Big data has evolved into huge, massive, and enormous data, and organizations are struggling to keep on top of it all. 

It’s clear to everybody that the more quality data you can handle in a reliable, accurate manner, the greater your understanding of your market, the better you can stay ahead of trends, the more confidently you can make business decisions and the larger your competitive edge over your rivals. 

As a result, the stakes are high when it comes to scaling data management. You need a data environment that’s strong and robust enough to gather all the data you need, verify and clean it, process it, and produce timely insights for your stakeholders to consume. 

With data continuing to grow and evolve, you can’t be complacent about your capabilities. If your data management system is sufficient for today, you need to be looking ahead and preparing to make sure that it can scale successfully for whatever might come along tomorrow. Here are 5 tips that enterprises need to know to successfully scale their big data environments. 

  • Adopt the right software 

The first and most fundamental challenge in a data management environment is gathering data from all the sources at your disposal, and ensuring that you have the tools to clean, verify, and deduplicate it so that data quality is high. 

Enterprises have to grapple with data that arrives in many different file formats and numerous different types, such as structured and unstructured data. As datasets become more complex, the number and complexity of the interrelationships between them rise exponentially, compounding the challenge. What’s more, different analytics tools prefer data in particular file formats, so you can’t just store all your data in a single format. 

Software like Spark, Hive, and Hadoop is designed expressly for dealing with big data problems. These tools can unite data from numerous sources and hold it in multiple different formats, supporting whichever analytics tools you choose and allowing you to filter, transform, and process data on demand. 

  • Introduce analytics databases 

As you furnish your data environment, it’s important to keep speed in mind. Some enterprises in fast-moving verticals need to be able to access time-critical insights that draw on real time or near real time data, so that they can keep up with changes in markets or operations. 

But even companies for whom timeliness is not a business-critical factor need a robust, powerful system that can provide swift responses to queries and also support multiple users without dragging or crashing under their weight. 

This means choosing elements that play well together, but also selecting specialized timeseries, elastic, or hybrid timeseries databases such as Clickhouse vs Druid which can support faster responses

  • Minimize segmentation

It’s understandable that every one of your departments wants their own data platform that’s optimized to meet their specific needs, but there’s no realistic way to meet all these demands. The more separate data platforms you provide, the greater the risk of data silos and/or data breaches emerging, and the harder it will be for your ITOps and data science teams to manage them all. 

To keep security high and maintenance efforts low, begin by uniting business users around sets of capabilities and services, and segment the use cases according to whether they need an analytics platform, or a transactional platform. This leaves you needing to support only two different approaches. 

However, don’t fall into the trap of building two separate data environments. Build in friction-free data exchanges between any parallel elements to make them interoperable, so as to avoid the creation of duplicate versions of truth. Choose tools like a cloud data warehouse that can work with a range of analytics platforms, so that teams can hook up their own preferred analytics to the communal data pool. 

  • Ramp up your hardware 

All the databases, analytics programs, and data processing software that makes up an enterprise data environment are highly memory-hungry, so you’ll need to ensure that you have sufficient memory space for all your data storage and processing needs. 

Decide early on whether you plan to scale up, or scale out. Scaling up means adding to your existing computational power, while scaling out means using distributed architectures where you either use a distributed third-party service, or you add to your own distributed servers. 

Moving your hardware to the cloud can help dispense with worries that you might suddenly run out of CPU power or disk drive space. Cloud CPU-as-a-Service gives you flexibility to scale at will while keeping costs down, because you only pay for the power you use, and can easily increase it in a hurry. 

  • Remove barriers to query

As you prepare to scale your data environment, don’t overlook the human element. A successful data management system is one that stakeholders can use effectively and efficiently, not just one that can keep on top of your data. 

You need analytics platforms that are intuitive and easy to use, so that employees without a technical background can run queries and access insights independently. Otherwise, you risk creating a situation where your data scientists are constantly responding to query requests, and your stakeholders can’t move on with their projects because they’re waiting for data science to have time to answer them. 

Look for user-friendly business intelligence (BI) tools that integrate smoothly into your system and remove the barriers to advanced insights. 

Scalability is possible for growing enterprises

Scaling your data environment is vital for business success in an increasingly data-powered world. By choosing the right software, databases, and flexible compute power, choosing easy-to-use analytics tools, and ensuring that your data platforms are interoperable, you can build an effective and future-proof data management system that gives your business an edge over your competition.