Big Data Analysis

While much of the value provided by the biggest tech companies comes from their big data, other firms struggle to obtain similar value from their data stores and streams. Companies are swimming in data today, but without easy-to-use tools for big data analysis, extracting real value from this data is a challenge.

With the right Business Intelligence (BI) solution however, organizations can quickly and easily load, combine, and examine different types of data from a variety of sources, extracting powerful insights that can be used to support smarter decision-making and problem solving, drive innovation, and spur growth. When big data analysis becomes more common and widespread in the organization, firms will then be able to use big data as a real competitive advantage.

The demands of big data analysis: barriers to implementation

The reality is that many firms face significant barriers in adopting big data analysis and turning big data into an asset. These include the lack of skilled employees, the high cost of hiring data scientists and big data experts, the difficulties of compliance regulation and data governance, data quality issues, and not knowing how to make big data usable to their workforce.

Big data analysis also demands the use of specialized technologies and techniques. Sophisticated analytics platforms and architecture are necessary for the handling and processing of massive, multi-sourced datasets. Examining large and complex data sets requires the use of statistics and advanced data analytics* including:

  • Machine learning, which is used to find patterns in data and quickly and automatically build models that can analyze high-volume, high-variety data sets and provide faster, more accurate results
  • Data mining, which is used to identify consistent patterns or systematic relationships between relevant variables in data and apply detected patterns to new datasets in order to generate predictions
  • Predictive analytics, which uses statistical algorithms and machine learning techniques, including predictive modeling, to determine the probability of future outcomes based on historical data
  • Text mining, which uses machine learning and natural language processing technology used to analyze social media content, documents, emails, surveys, and other text-based data sources

*What is data analytics? It refers to the process of or the use of technology for transforming raw data into useful insights—organizing, modeling, and visualizing data for the purpose of identifying meaningful patterns, correlations, anomalies, and trends.

Using business intelligence software for big data analysis

By incorporating advanced data analytics techniques, the newest generation of business intelligence software now makes it easier for organizations to carry out big data analysis—to surface hidden patterns, relationships, and other insights from previously untapped datasets. Sophisticated self-service BI solutions are putting advanced analytics tools in the hands of everyday business users, from customer service representatives to C-level executives. Users across the organization are empowered to analyze and make sense of big data.

Robust BI and analytics platforms are designed to efficiently collect, display, transform, and process large sets of structured, semi-structured, and unstructured data. Companies can now use these platforms to easily capture, combine, manage, process, and derive value from all the data flowing into their organization from connected devices and sensors, equipment log files, transaction data, call records, location-based services, video archives, and many other data sources. Business users can use big data analysis for business analysis, applying it in a wide variety of use cases: market research, trends analysis, customer segmentation, marketing campaign analysis, sentiment analysis, financial risk analysis, fraud detection, price optimization, demand forecasting, predictive maintenance, and more.

Trends in big data analysis

In recent years, we’ve seen the emergence of some significant trends in the world of big data and data analytics, trends that are introducing more challenges as well as opportunities for companies wanting to implement big data analysis. These include:

  • Real-time analytics: Ultra-connectivity, cloud infrastructure, and powerful streaming architecture are encouraging more firms to incorporate continuous intelligence into their workflows, using real-time data insights to inform decision-making.
  • Wide data: As data formats become more varied and fragmented, organizations must find ways to deal with highly distributed “wide data”. Companies that can connect to and synthesize varied, fragmented data will achieve a competitive edge.
  • Active metadata catalogs: As organizations struggle to inventory and synthesize distributed, diverse data assets, demand for enterprise-wide data catalogs is on the rise, and one promising solution is machine learning-augmented metadata catalogs.
  • Data literacy as a service: More businesses will find they need to partner with solution vendors to scale data expertise, essentially leveraging a combination of software, education, and support as a service to foster data literacy.
  • Multifaceted interaction: Now that AI-enabled technologies have made possible new ways of interacting with data such as conversational and visual analytics, more immersive, multifaceted user-device interaction may be right around the corner.

Big data analysis made simple: Scalable, flexible, intuitive solutions from Qlik

Qlik’s big data management and analytics platform is an end-to-end, real-time data integration and analytics solution that is helping firms turn their massive datasets into actionable insights. Qlik’s solution for big data analysis is the only platform on the market allowing for open-ended, curiosity-driven exploration by users of all skill levels.

All your data, whether in a data lake or legacy application, can be put into a governed enterprise catalog so that users can “shop” for the data they need—without compromising your security policies with Qlik Catalog™. And with Qlik Sense® as your front end for big data analysis, —putting big data in the context of your entire business and allowing your users to dive into big data via interactive visual dashboards and natural language searches.

With Qlik, you can make big data analysis accessible to teams across your organization and place big data in the hands of those working closest to your operations. Support all major BI use cases—from data discovery and visualization to executive reporting and dashboards. Data governance and management stay in the hands of IT while users can freely explore, experiment, collaborate, and make impactful discoveries.

FAQs

How do you analyze big data?

It can be analyzed directly in a Hadoop cluster or run through a processing engine such as Spark or loaded into a suitable analytics tool or platform, like Qlik, where it can be analyzed using sophisticated algorithms, statistical models, and other advanced data analytics techniques including machine learning, data mining, predictive analytics, and text mining.

What are the benefits of big data?

Businesses using big data analysis can solve complex business problems faster, better understand their customers and customer behavior, maximize operational efficiency, reduce spending, accelerate strategic decision-making, boost productivity, increase agility, and drive innovation.

How can big data help businesses?

It helps businesses detect and prevent fraud, decipher market trends, personalize the customer experience, develop better products and services, boost marketing ROI, optimize pricing structures, create more effective supply chains, and enhance predictive maintenance.

Learn more about Qlik’s big data management and analytics platform.

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