The Art of Big Data Management: What is it (and Why Does it Matter)?

Big data represents the evolution of business information management, following on from data federation and virtualization. What were once discrete large databases, spreadsheets, content files and production datasets have expanded in size and growth rates beyond the capabilities of traditional data management.

Big data not only defines the data volume and the increase in speed that it arrives, but the modern analytics and management tools to enable businesses to handle it effectively. The benefits are improved and more timely insights into operations, leading to better strategic and decision-making processes.

Not adopting the tools that can handle big data will leave a business increasingly in the dark about what data it has, struggling in its ability to gain insights from it and at risk of compliance failures, hacks and loss of critical information. 

Every business should carefully map big data implementation and plan out its strategy, because the growth in data will never slow.

Read on to find out:

• What is big data management?
• The benefits of big data management
• What’s the difference between traditional data and big data?
• 5 common big data management challenges
• 5 big data management best practices
• Final thoughts

What is big data management?

For businesses, big data management is the use of applications and services to organize business information, including structured and unstructured data. There’s no minimum size for data to qualify as ‘big’, but typically it refers to any data that’s impractical for a traditional database to manage, and as long as there’s enough data to generate valuable insights.

Big data applications enable data managers to administrate, ensure data quality, provide compliance and governance and deliver visualizations, while providing headroom for the businesses’ data footprint to grow – without causing harm to the company.

As the volume of big data grows, businesses that don’t adopt big data management risk losing sight of where they’re headed and will struggle to manage future data growth. Data management problems are a critical issue, with inaccurate, lost or duplicate data more likely to cause issues as the volume grows.

For those adopting big data management as early as possible, as part of data science or digital business initiatives, they’ll be better positioned to avoid those perils and reap the benefits with strong data quality leading to greater-value business intelligence insights.

Data management tools use analytics and AI for operational and strategic planning across many functional business areas from accounting, auditing, legal work and production to supply chain and customer satisfaction. This allows them to identify internal business trends that can lead to efficiency savings, new ideas and concepts that will generate great value.

The benefits of big data management

Big data management offers a range of insights across all markets, verticals and business sizes, with smaller firms able to use AI and analytics on outsized data sets that their predecessors would find impossible or unaffordable to manage. For enterprises, the benefits include:

• Data-driven innovation: The quest for R&D and product innovation is vital for VC funding, driving business growth and hiring the brightest talent. Data scientists can deliver understanding and insights that can drive the business forward, whatever the market.
• Better market intelligence: Automated data management can help:
  • Analyze the behavior of your supply chain and that of rivals
  • Explore customer and client dynamics
  • Perform faster and deeper competitive analysis, with information that can improve sales, marketing and product design efforts to better target specific audiences among your customers and prospects
• Improved customer insights: The customer might always be right, but with millions of variations of ‘right’ across their experience and needs, it takes modern data management and analytics to figure out what the best ‘right’ is for each category and sub-type of customer, branching across your own data sources and external data like credit information or social media feedback.
• Smarter business operations: The smallest of individual savings across a large business can multiply to huge sums, and data management helps deliver insights across HR, benefits, production, warranty management, fraud detection and many other areas.
• A future-proof business: Data is only going to get bigger and flow into businesses faster through smart warehouses, IoT production, edge networks and clouds. Being able to handle whatever data streams come along next will position any business well for that future and the growth and benefits that will come with strong data management efforts.

What’s the difference between traditional data and big data?

Traditional data

Traditional data comes from monolithic applications like databases and spreadsheets, a time long before the phrase ‘big data’ was coined. Their structured data was only designed for inflexible access within that application, with little thought for exporting or smart dashboards. Examples include SQL parts files with fixed fields, customer or employee spreadsheets and even relatively modern applications like WordPress content management data or the chats stored from Teams meetings.

Big data

Big data might not be massive in terms of the number of records, but it has several defining characteristics compared to traditional data types. Originally there were three, but as with everything big data, that number continues to grow):

The 6 Vs of big data 

1. Volume: This refers to the quantity of data that needs to be stored to keep any analysis current and meaningful. That could be adding a million retail transactions an hour or an ongoing analysis of multiple market trend data points.
2. Velocity: This refers to the speed at which new data arrives, for example updating a production line database with 7,000 different sensor metrics, 30 times per second, 24 hours a day.
3. Variety: This refers to the types of data coming into the database. Consider the number information for a regional or national automatic number plate recognition (ANPR) system, providing the car photo, the data version of the number plate, time and date details and perhaps weather conditions, plus a link to the registered driver’s ID.
4. Veracity: The quality of data is key to any attempt to analyze it. Metrics from medical systems or weather measuring stations can be considered of high veracity, but a stream of social media posts related to reactions to a product launch or freeform text feedback is likely to be of lower veracity.
5. Variability: Highly variable data is that which could be inconsistent or struggling for consistency. For example, different ingredients used in a food factory changing the end results or intermittent updates from legacy pump measuring equipment that could impact the speed of data all add its potential variability, adding confusion to any analysis.
6. Value: Big data business efforts must produce some value, each data stream could have its own inherent value, which may only be apparent after the fact. But estimates can be put in place as experience with big data grows, and as insights are gained from multiple data sources, they can generate outsized value and deliver broader business benefits.

5 common big data management challenges

Data management remains a major effort for any IT department or data team, but is still easier to handle than previous federation and virtualization efforts. The primary challenges faced include:

1. Understanding technology and applications

As with any IT evolution, database and management services bundle new features at pace to deliver business benefits. There are also the legacy elements of data management such as ELT and ETL that still play a major role in most data applications, with team leaders needing to establish what applications are best and where they fit within various processes.

2. Ensuring data quality

Related to data veracity mentioned above, the better the quality of data that enters any big data effort, the more realistic any predictions or recommendations will be. Cleansing data, data preparation and other tasks, especially for legacy files with many years of entries, should be a priority.

3. Working with data science professionals

IT and technology departments have expanded to incorporate all sorts of engineers, scientists and gurus. Data scientists should play a key role in any big data effort, with their deep knowledge of cleaning and extracting the value from various types of data.

4. Ensuring compliance and protection

Data is valuable beyond your business. Hackers, international competitors and other actors will make extensive efforts to access it. Every enterprise must follow their industry regulations on compliance and data protection, and big data teams must ensure appropriate people are responsible for data governance, protection and reporting.

5. The cost of it all

Among the promises of the cloud, cheap data is one, but as big data scales, processing cycles are consumed, teams grow and new services are added, the costs can soon add up. Maintaining tight control over big data costs is essential so that the business value of the benefits isn’t swallowed by reaching those goals.

Learn more: 6 Terrifying Big Data Challenges (and How CDOs Can Solve Them)

5 big data best practices

Big data isn’t an issue to be treated lightly by any enterprise – it requires a thoughtful approach to planning and delivering the benefits. The following best practices should all be considered as part of the process.

1. Plan around business goals, not IT choice

Many enterprises still follow the IT department’s lead when it comes to adopting technology solutions. For big data, the focus must be on delivering business results. This requires identifying suitable data and aligning it to business goals by adopting the most useful analytics tools to deliver the best types of analytics and recommendations.

Those goals might include direct deliverables such as “deliver data to improve our sales pipeline and target personas”. Alternatively, it can have a broader context, such as verifying expert or business leader intuition to provide evidence that can back up decision making processes. 

2. Accept data in all its forms

When a data audit reveals huge amounts of previously invisible data, don’t elect to dump it immediately. Instead, establish its value and that of requests for new databases, even massive data sets that sound scarily large, as automation and AI will take on most of the analytic workload. Be prepared to store data too, as it may come in useful for pattern and other analysis that might not be immediately obvious.

Yes, the digital effort and cost of storing, governing and managing data may add up, but the value in big data is likely to be many times more as new use cases for it, or tools arrive that enable new types of analysis.

3. Ensure business leaders see the value of data visualization

Whatever the enterprise, however much data there is and all the cool tools to analyze it, those leading a data management project should limit the operational team or business leaders to the results shown through data visualization.

Not only are the sparkling charts easier for them to digest, but they help describe the value on offer, especially when it comes to predictive analytics and the results of recommendation engines based on your big data.

4. Ensure governance and compliance are built-in from the start

Any regulated industry, such as health and finance, will already have a raft of data legislation and guidance to follow, plus the likes of PDGR or the California Consumer Privacy Act. Big data management tools have features to ensure this guidance is followed and should be adopted from day one to protect the business from reputational or financial cost in the event of a breach or leak.

5. Move the business to real-time

Many enterprises still rely on monthly or quarterly reports from their data analysis, even though big data is capable of delivering live information. Where the benefit is clear, reconfiguring the business to act on live information to address fast-moving trends such as pricing changes, supply chain issues and other areas can deliver great benefits and get the whole organization moving and thinking faster. 

Final thoughts

After generations of struggles to deliver on data federation and virtualization efforts, the automated nature of modern big data finally gives enterprises the chance to extract the maximum value from their data. While the process isn’t as trivial as a quick cloud upgrade or database upgrade, the benefits are proven in the wild. And, as AI-led big data becomes the dominant form of doing business across many markets, those left behind will find that gap increasing rapidly.