In our current world, technology is a key driver and one of the largest disruptors in the operational functions for a business. Therefore, it’s no surprise that the goal for many companies is to develop faster, more efficient and productive business processes. In response, companies are shifting toward the adoption of new technologies to better record data as well as analyze and understand its implications for overall performance.
With these new technologies comes new types of data management and automation, processes and systems across different departments are integrated (including finance, information technology and operations), data is efficiently collected and data points are leveraged to maximize analytics capabilities.
What are big data and data analytics?
To understand the benefits of data analytics, we must first define the term big data. Big data refers to the study and applications of data sets that are too complex for traditional data-processing software applications to adequately manage. Big data is traditionally defined with primary attributes, namely – volume, velocity and variety, with overabundance in each category. Modern usage of the term tends to refer to the use of predictive analytics, user behavior analytics or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Therefore, data analytics refers to the mining of data to identify patterns, the using of programming and statistical analysis and leveraging historical data to create business intelligence and perform advanced analytics. Business intelligence reflects the current state of data and is useful when preparing reports and dashboards to visualize data in an ad-hoc manner. Advanced analytics is the analysis of data to produce helpful insights outside of business intelligence, which can aid in forecasting, identifying trends and analyzing unstructured data.
At its most basic definition, data analytics can be simply defined as how we extract, analyze and consume the insights from large amounts of data, which is applicable to all parts of a company’s operations processes. Not only can data analytics bring understanding to how a company operates in the present, such understanding can also help in developing foresight for the future. For example, you can read more here about sentiment analysis and its capability to study unstructured response data through machine learning, which may help companies better understand and respond to customer feedback regarding a product or service.
In a similar way, companies can also leverage data to improve the functionality of their operations, specifically by identifying areas that can be streamlined as either low touch or no touch (i.e., little to no direct human involvement). By investing in the right infrastructure, resilient processes can be created through automation to potentially yield rich dividends during times of prosperity or economic downturn. This means more time may be spent analyzing data and using it to provide insight into performance and forecasting for future goals. Companies may consider some of the following technology enablers to add as part of their automated infrastructure to enable big data:
- Predictive analytics process big data in order to help businesses understand predictive scenarios.
- Data warehouses store and analyze petabyte-size files to develop parallel programs with simplicity, therefore optimizing security, auditing and support.
- Cloud computing offers faster innovation and flexible resources regarding certain computing services, including servers, storage, databases, software, intelligence and networking. Cloud computing specifically lowers operating costs and helps infrastructure run more efficiently.
- Stream analytics helps to process and store real-time data on multiple formats and platforms.
What are the benefits?
One of the more significant benefits to utilizing big data technology enablers is that value can be added back to an organization by increasing the amount of time available to devote to strategic activities. The opportunity to streamline and better document what is happening within the business will also add value by eliminating duplicative and repetitive process steps, making processes more efficient and less prone to any potential reporting errors. Data can also be leveraged across many functions in order to produce a master data set, which can act as one source for data. Other value-adding benefits include the following:
- Data becomes accessible for multiple purposes
- Large volumes of data are processed more quickly
- Data becomes more scalable
- Shared data increases collaboration across departmental groups
- Dashboards for data become easier to read
- One location exists for all data sources for global use
- Data document governance is more consistent
With proper management and implementation, as well as the right technology enablers, businesses can improve how they collect and analyze data to build better business intelligence and analytics to guide future performance. As operational efficiency improves, so will the ability to make strategic decisions with the rightly curated information at hand.
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