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big-data-analytics

How Big Data Analytics and Data Management Can Fuel Business Growth

13:52 14 April in Blog
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Big Data technology in today’s world

Did you know that the big data and business analytics market is valued at $198.08 billion (as of 2020)? Or that the US economy loses up to $3 trillion per year due to poor data quality? Every day, internet users generate 2.5 quintillion bytes of data which means an average person generates over 1.5 megabytes of data every second?

Have you read any of the case studies involving how Netflix and Spotify leverage big data for creating unique customer experiences? If you have, this will be easier to explain and if you haven’t, I would say, they are worth a read. They tell you how big data helped them create a mark in today’s world.

The same cannot be told for everyone but all the major players in every industry including financial institutions, supply chain logistics, E-commerce, food tech, Edutech, cab aggregators, and entertainment media have all leveraged big data to expand their market presence and more importantly, ensure the sustainability of their business in the long run.

FedEx, the world’s leading courier company, ships over 17 million orders a day, globally. One can only imagine the amount of data they accumulate per day. But it doesn’t end there. FedEx along with the data of orders, merges these with weather and traffic data. Tesla is another company that picks up data from their cars and also analyzes traffic and weather. FedEx leverages data to improve its supply chain resilience while Tesla improves product innovation.

With big data, brands want to improve their value offerings. Big data helps companies stay relevant, stay on top of trends, prepare for the future, and adapt quickly to setbacks or disruptions.

Big Data Ecosystem

Big data paved the way for organizations to get better at what they do. Data management and analytics are a part of a massive, almost unseen ecosystem which lets you leverage data for valuable insights. According to Gartner, through 2025, 80% of the organizations seeking to scale their digital business will fail because they do not take a modern approach to data and analytics governance. Such is the significance of big data in today’s world.

A large number of organizations accumulate massive amounts of data almost every single day and analyzing every batch of data that comes in demands the use of modern tools and platforms. The comprehensive system which collectively includes generating data, storing the data, aggregating and analyzing the data, the tools, platforms and other software involved is referred to as Big Data Ecosystem. The larger the company, the more complex its ecosystem becomes.

Effective data analytics allows personalization in marketing & sales, identifying new opportunities, making important decisions and being sustainable for the long term.

Competitive Advantages to using Big Data Analytics

The return on investment is a huge concern expressed by a fair share of businesses when it came to leveraging big data. The truth is that with a clear vision and resources, companies can benefit a great deal from big data.

  1. Customer Experience

    If you know and understand a lot of details about every customer, won’t it make sales easier and relationships much easier? Customer experience and behavior can be tracked and analyzed for providing personalized experience to clients.

  2. Operational Efficiency

    Be it supply chain resilience, staff management, trend identification, budget planning, risk and fraud management, big data increases efficiency by making data-driven predictions and forecasts.

  3. Product/Service innovation

    With adequate market intelligence, big data analytics can be used for unearthing the scope for product improvement or innovation. Netflix uses big data to make decisions on new productions, casting and marketing and generate millions in revenue through successful and strategic bets.

Data Management

Before building a big data ecosystem, the goals of the organization and the data strategy should be very clear. Otherwise, it will result in poor data quality and as previously mentioned, cost over 3 trillion dollars for an entire nation. 3 trillion is more than the GDP of the United Kingdom, which is the 5th largest economy in the world!

Ensuring rich data quality, maximum security & governance, maintenance, and efficiency in storage and analysis come under the umbrella term of Data Management. With the amount of data being accumulated, it is easier said than done. There is a wide range of problems that are presented to organizations when working with big data.

Challenges associated with Data Management and Optimizing Big Data

    1. Unscalable data architecture

      Scalable data architecture is not restricted to high storage space. It includes data generation, aggregation, analysis and governance. When they are not in sync with each other, the problem begins and by then, allotting the budget, reevaluating and reworking will take a considerable amount of time which will cost the firm further capital and productivity.

    2. Poor data quality

      Inaccurate data and data inconsistency causes poor data quality. Typically, it is human error that causes inaccuracy and inconsistency but there are several other ways unique to the organization that lead to this. For example, one company let all its data scientists access and make changes to their data tables for report generation, which caused inconsistency and cost the company significantly. The best way to avoid poor data quality is to have a strict data governance system in place.

    3. Slow query performance

      The majority of the data a business has stored is generally unstructured. Most of these are accumulated in data silos or data lakes. The only issue is that sometimes one of the tools used in the process might have slow processing speeds. This means queries for large data sets might take days or eventually fail. It is very important to evaluate the tools while setting up and scaling the big data ecosystem.

    4. Challenges with Cloud

      Data migration, data center migration, onboarding to the cloud, technical errors, and security leakages are a few of the challenges that come with storing big data on the cloud. Make no mistake, the cloud is an excellent platform to store and analyze data. Plus, it is very cost-effective compared to on-premise. But without proper management, any part of the big data ecosystem can be prone to issues.

    5. Security and Privacy

      The magnitude at which data is being accumulated and used today for decision making leaves a lot of room for security errors. And a majority of the security breaches in organizations are actually caused by human error. Security and privacy as a challenge escalated in 2020 when cyber theft and frauds were at an all-time high. According to IBM, on average it takes 228 days to identify a security breach and 80 days to contain it. As a result, the data of millions of people have been exposed in the past and it increases the privacy concerns of netizens.

    6. Unstructured Data Management

      Analyzing unstructured data is vital since it holds a dearth of crucial information. While unstructured data can provide deep insights, it is equally difficult to manage and store it. Unstructured data is typically stored in data lakes. Currently, there is a labor shortage of talented data engineers who manage and optimize big data storage in terms of market demand.

Solutions for Big Data Management

Despite the challenges, the odds are certainly in favor of big data. Strong implementation of big data strategy, architecture, governance and management will reduce the risks of prominent challenges to big data analytics.

Big Data Storage Optimization

As mentioned, big data storage optimization is a tedious task with a lot of room for security errors if not carefully monitored. Currently, in the market, organizations look at on-premises, cloud storage, hybrid and multi-cloud storage options based on the kind of data they have and decide between data lakes, data warehouses or both depending on the kind of data they have and their long term goals.

Enterprise Big Data Strategy

The enterprise big data strategy encompasses the vision and road map for a company’s ability to generate, store and leverage data to meet its vision or objectives. It includes all domain-specific strategies such as master data management, artificial intelligence and business intelligence. It does not work as a constitution but rather works as an evolving framework for smooth operations and governance. Businesses that prioritize the ‘data-first’ approach spend a lot of time working on their enterprise data strategy, revising it from time to time.

Data Governance

Data governance is the framework organizations use that encompasses the collection and usage of data, along with security, ethics, policies, and other aspects. Strong data governance is essential to uphold the quality and security of the data. Without adequate data governance measures, enterprise data may be at high risk, even internally. Human error is one of the main reasons behind data breaches in organizations. Enterprises do not stress much on the importance of data governance. Without upskilling their workforce and implementing strict measures, your big data is not safe and it can cost you in the long run. Data governance is also the solution to security, privacy, data migrations, data quality and effective data management.

Another way to mitigate risk is opting for managed services and resource sharing. Hiring the right big data and BI partner significantly minimizes such threats. Talk to companies about the experience their team holds, their previous projects and what they can do for you as a partner.

Hope the blog was insightful. If you have any questions or feedback, you can comment below and we will answer your questions. If you are looking for big data solutions or services, then we would recommend contacting us or leaving your contact on our page. We will get in touch very soon. Thank you for reading.

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