Big data is a powerful tool that companies can use to gain a deeper understanding of their customers, their products and the market in which they operate.
By harnessing big data, companies can make more informed decisions, optimize processes and predict future trends. Let’s take a look at how big data can be used to improve business operations.
What is big data?
Big data is a combination of structured, semi-structured and unstructured data, usually collected by organizations and large companies, that can be mined for insights and used in machine learning projects, predictive modelling and other advanced analytics applications.
Systems that process and store big data have become a common component of data management architectures in organizations, along with tools that support the use of big data analytics. Big data is often characterized by the three Vs:
- the large volume of data;
- the wide variety of data types that are stored in big data systems;
- the velocity with which much of the data is generated, collected and processed.
These characteristics were first identified in 2001 by Doug Laney, then an analyst at the consulting firm Meta Group Inc. Gartner further popularized them after acquiring Meta Group in 2005. More recently, many more Vs have been added to various descriptions of big data, including veracity, value and variability.
Although big data does not correspond to a specific volume of data, big data implementations often involve terabytes, petabytes and even exabytes of data created and collected over time.
Why is big data important?
Businesses use big data in their systems to improve operations, provide better customer service, create customized marketing campaigns and take other actions that can ultimately increase revenue and profits. Businesses that use it effectively hold a potential competitive advantage over those that do not because they can make much faster and more informed business decisions.
For example, big data provides valuable customer insights that businesses can use to fine-tune marketing, advertising and promotions to increase target engagement and conversion rates. Both historical and real-time data can be analyzed to assess the evolving preferences of consumers or corporate buyers, enabling companies to become more responsive to their wants and needs.
Big data is also used, for example, by medical researchers to identify signs of disease and risk factors and by doctors to help diagnose specific diseases and conditions in patients. In addition, a combination of data from electronic health records, social media sites, the web and other sources can provide health organizations and government agencies with up-to-date information on infectious disease threats or outbreaks.
Here are some other examples of how big data is being used by organizations:
- In the energy sector, big data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; similarly, utilities use it to track electricity grids.
- Financial services companies use big data systems for risk management and real-time analysis of market data.
- Manufacturers and transport companies rely on big data to manage their supply chains and optimize delivery routes.
- Other government uses of big data include emergency response, crime prevention, and initiatives to make our cities smarter.
Other examples of Big Data
Big data come from a myriad of sources: examples include transaction processing systems, customer databases, documents, emails, medical records, Internet clickstream logs, mobile apps and social networks. They also include machine-generated data, such as network and server log files, as well as data from sensors on production machines, industrial equipment and Internet of Things devices.
In addition to data from internal systems, big data environments often incorporate external data on consumers, financial markets, weather and traffic conditions, geographical information, scientific research and more. Images, video and audio files are also forms of big data, and many applications of these involve streaming data that is processed and collected on an ongoing basis.
The ‘Vs’ of Big Data
Volume is the most commonly cited characteristic of big data. A big data environment does not have to contain a large amount of data, but most do due to the nature of the data collected and stored. Clickstreams, system logs and stream processing systems are among the sources that typically produce huge volumes of data on an ongoing basis.
Big data also includes a wide variety of data types, including the following:
- structured data, such as transactions and financial records;
- unstructured data, such as text, documents and multimedia files;
- semi-structured data, such as web server logs and streaming data from sensors.
Various types of data may need to be stored and managed together in big data systems. Moreover, big data applications often include multiple data sets that may not be integrated in advance. For example, a big data analysis project may attempt to predict product sales by correlating data on past sales, returns, online reviews and customer service calls.
Velocity refers to the speed at which data is generated and needs to be processed and analyzed. Big data sets are often updated in real-time or near real-time instead of the daily, weekly or monthly updates performed in many traditional data warehouses. Data velocity management is also crucial as big data analysis expands further into machine learning and artificial intelligence (AI), where analytical processes automatically find patterns in the data and use them to generate insights.
Other characteristics of big data
Looking beyond the original three Vs, here are details on some of the others that are now often associated with big data:
- Veracity refers to the degree of accuracy of data sets and their reliability. Raw data collected from various sources can cause data quality problems that are often difficult to detect. If not corrected through cleaning processes, erroneous data leads to analysis errors that can undermine the value of business analysis initiatives. Therefore, data management and analysis teams must ensure that they have sufficiently accurate information to produce valid results.
- Some data scientists and consultants add the value component to the list of characteristics of big data. Not all data collected has real value, and not all of it leads to business benefits. Consequently, organizations need to confirm that the data relates to relevant business issues before using it in big data analysis projects.
- Variability also often applies to big data sets, which may have multiple meanings or be formatted differently in separate data sources, a factor that further complicates the management and analysis of big data.
The big data challenge
In connection with processing capacity issues, big data architecture design is a common challenge for companies and institutions. Implementing and managing big data systems also require new skills than those typically possessed by database administrators and developers.
Both of these problems can be solved by using a cloud service, but IT managers must keep an eye on its utilization to ensure that costs do not get out of hand. In addition, migrating local data sets and processing workloads to the cloud is often complex.
Other challenges in managing big data systems include making data accessible to data scientists and analysts, especially in distributed environments with different platforms and archives. To help analysts find relevant data, data analysis and management teams are increasingly creating catalogues incorporating metadata management and data derivation functions. Integrating big data sets is also often complicated, particularly when the variety and velocity of data are determining factors.
The key to an effective big data strategy
In an organization, developing a big data strategy requires an understanding of the business objectives and the available data, as well as an assessment of the need for additional data to achieve the objectives. Steps include the following:
- prioritizing use cases and planned applications;
- identifying new systems and tools needed;
- creating a deployment roadmap;
- assessing internal competencies to see if retraining or hiring is necessary.
To ensure that big data sets are clean, consistent and used correctly, a data governance program and related data quality management processes must also be prioritized. Other best practices for big data management and analysis include focusing on business needs versus available technologies and using data visualization to facilitate discovery and analysis.
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We bring value to your decision-making and work organization through our IT expertise. Our challenge is to help you make your ideas a reality, in a simple and optimized way, through the support of technological innovation.
Here is how Demiware can meet the IoT needs of your industry sector:
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