Managing big data is one of the top challenges facing businesses today. With an average of 2.5 quintillion data bytes generated per person daily in 2020, organizations need to know how they can turn this vast volume of data into information they can use for decision-making.

Drawing big data insights may have been previously confined to those with technical or statistical expertise. But that's changing now with the rise of free or affordable big data analytics tools. Results from such analyses can then help business leaders determine their in-store or e-commerce platform's peak traffic times, flops and best sellers, and website usage, among others.

Benefits of Big Data

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The information generated by big data software can increase your competitive advantage over other players in your industry in the following ways:

  • More delightful customer experience
    Consumers currently expect a highly personalized experience from their favorite brands. Thus, big data solutions become necessary in unearthing clients' values, pain points, and trends from your customer relationship management system, social media, and email transactions. This way, you can also enhance engagement, which leads to client loyalty.
  • Better decision-making
    Getting hold of data about your customers' behavioral tendencies and preferences can help you craft better marketing messages or enhance your products and services. Consumer data can also help business leaders improve forecast and pricing decisions.
  • Error and fraud detection
    When big data and machine learning are combined, you get a powerful tool that can detect irregular or suspicious transactions, anomalies, or failures. This can preempt security breaches and fraudulent behavior.
  • Increased efficiency
    With big data tools, you can analyze large amounts of data faster, making you and your team more productive. Big data tools can handle external data on products, services, and customers as well as internal data on operations, allowing you to analyze and increase your organization's efficiency inside and out.

Big Data Opportunities in Various Sectors

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Real-time information from big data is essential to business growth in every industry. Let's take a look at its practical uses in every sector:

Retail and Advertising

Both e-commerce stores and brick-and-mortar companies can use tools to analyze their customers’ purchases, loyalty programs, social media, and more, which will provide more pleasurable and customized client journeys. Meanwhile, internal data offers a timely analysis of inventory and high-demand products.

Financial Services

Banks, insurance companies, and credit card firms can use big data systems for real-time market analysis, risk management, and loan and insurance policy approvals.

Manufacturing and Industry

Companies can glean information from current markets to better manage their supply chains, predict sales, and develop or innovate products. Businesses can also use internal data to conduct preventive maintenance that will reduce equipment downtime and failure.

Logistics and Transportation

Analyzing big data from GPS and social media allows couriers and service providers to plan routes as they learn about traffic jams and other road conditions in advance. This translates to savings in fuel and time.

Healthcare

Big data analytics can help doctors identify symptoms of serious illnesses early and diagnose patients more quickly and accurately. This way, treatments will be less complicated and less expensive. Hospital and clinic data can also help administrators keep track of equipment used and determine patient experience to offer personalized treatment plans.

Travel

Travel agencies and websites can use mobile and web logs as well as social media data to discover preferences. In doing so, tour providers can personalize recommendations and upsell appropriate products and offers.

Energy

The utilities sector is now making use of big data analytics to monitor electrical grids, manage service outages, dispatch crews, and even identify possible pipeline and drilling operations for gas and oil.

Public Sector

Local government units can tap big data features to track area safety, prevent crime, and deploy emergency response teams.

Communications and Entertainment

Telecommunication, internet, and streaming companies can analyze machine- and customer-generated data to gain, retain, and even widen their existing client base. For instance, through big data analytics, gaming firms can learn about their users' preferences or issues across products and build their future offerings from these insights.

How to Leverage Big Data

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To develop a business strategy for optimizing big data, you need to take the following steps:

1. Clarify your organization's goals.

Detailing your objectives will help you design the appropriate strategy. For instance, if you wish to expand your market share over a particular location, you'll need to take advantage of zip code-based data. Meanwhile, you may have to conduct consumer profile analytics to determine who your potential buyers are within a particular target demographic.

2. Identify your existing data sources and assess how you currently use data.

Your company's customer touchpoints such as your payment forms, website, and social media accounts are just some of the data sources you can use to discover what your clients' current buyer journeys are like.

Are you already reviewing click rates, page visits, and other behavior on a regular basis to determine what makes your visitors and customers skip certain pages of your website or go after certain products at your e-store? You may also have to verify whether you're already using data from your CRM system, in-store traffic monitoring, and third-party promotional tools to improve engagement and promote sales.

3. Determine and prioritize big data use cases in line with your business goals.

As an example, if you wish to reduce your e-commerce site's cart abandonment, you should develop a strategy that will prioritize the analysis of big data related to your payment page. This will allow you to find out what aspects of your checkout, such as long forms to fill and lack of transparency about costs, cause shoppers to leave your site without completing their purchase.

4. Formulate a roadmap to carry out your big data management plan.

  • Identify the tools you need to process your big data.
    Some of the important qualities to look for in a big data management tool include:
    • Capacity to process a high volume of raw data
      Effective big data solutions should have file exporting, data mining, and data modeling features as well as data visualization for presenting information in different types of graphs.
    • User-friendly interface
      Choose a program with an interactive dashboard that even non-IT colleagues can understand and access. Its controls should let users drag and drop selected statistics or figures on an empty page and filter these figures for segmentation, analysis, and graphical presentation.
    • Reporting features
      Big data software can help you stay competitive through reporting functions that gather real-time data. This will aid teams in providing after-sales service to customers and answering queries.
    • Simple integration
      The big data platform you select should have toolkits and built-in connectors that will make it easy for you or your team to integrate the data with your existing business applications and data sources.
    • Embeddable insights
      Pick a big data system that can produce additional value (e.g., business insights), which you can easily incorporate into a decision-making platform or real-time event-streaming software.
    • Scalable system
      Your chosen tool should be able to handle an ever-expanding volume of data sets without requiring you to pay for expensive hardware or cloud services as your database grows.
    • Data security
      The best big data tool should have features that will support compliance with data privacy and security laws, including authenticating users, encrypting data, and anonymizing data when needed.
  • Assess your internal staff’s skills to determine hiring or training requirements.
    If you’re a large company with larger data sets, you may need to acquire data engineers and big data architects if training current IT teams won't be enough to map out and execute a big data strategy suitable for your organization.
  • Set up a data governance program.
    After getting executive buy-in, set up a data governance committee that will write clear data definitions, develop comprehensive policies (including cloud-based operations or processes), and supervise the documentation of these guidelines. Policies include assigning roles and defining responsibilities so that at any time, the organization knows who did what with which data.

With leadership support, the committee should be able to steer internal business units to integrate the policies within their teams. Your business may have to hold training on data risks and data breach prevention.

Potential Challenges in Big Data Management

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Some of the issues that may discourage businesses from adopting big data tools or strategies include:

Added costs

While providers now offer more big data analytics products at different price ranges, your business may have to factor in additional expenses to your budget. They can include fees for running software, outsourcing services for maintenance, and employee training. However, there are analytics solutions with machine learning that can take the place of professional recruitment or training.

Privacy concerns

Consumers are wary about brands and companies collecting and retaining their personal data. You need to be transparent about your data security policies. Meanwhile, you also need to ensure your data security is in full force for internal matters such as your company's trade processes, marketing strategies, competitive analysis, and sales plans.

Massive data

Up to 90% of data generated by companies is unstructured. This type of data remains in its native format until it’s used. The data includes consumer responses to open-ended survey questions, images from patients collected by medical imaging devices, recorded Zoom meetings, and social media conversations.

Cleaning, analyzing, and storing unstructured or semi-structured data needs time, effort, and planning. However, some companies are already using artificial intelligence to mine, index, and organize such data. There is also purging automation that helps them determine and drop unnecessary data.