Businesses today generate so much data requiring analysis that using an analytical approach makes sense. Understand what data analytics is, how itโs used, and why your business needs it.
This blog will help define what data analytics is, how it is used, and why your business needs it.
Data analytics defined
The simplest definition of data analytics is that itโs the process of analyzing raw data, with the intent of discovering patterns or trends, deriving insights, or drawing conclusions. The outcome of that analysis is then used to facilitate data-based decision making, such as making a change or improvement, or taking an action of some sort.
There are four key types of data analytics:
Descriptive analytics. This answers the question of โWhat has already happened?โ Descriptive analytics is one of the most basic types of data analytics that businesses use. Itโs done by analyzing historical data to understand changes that have already take place. The result from this analysis is then compared to a comparable period of time so that the change(s) can be understood in a contextual setting.
An example of descriptive analytics being used in a real-world setting are corporate financial reports, such as the change in the debt-to-equity ratio, stock price change over the last four quarters, or year-over-year revenue growth.
Diagnostic analytics. This answers the question of โWhy did something happen?โ Diagnostic analytics is often used by businesses as the next step after descriptive analytics to determine more specific information about the relationship between variables.
An example of diagnostic analytics in use is cause and effect correlations in a manufacturing environment. If the speed at which automated assembly robots are run was increased by 5% during one quarter, why did the overall facility output decrease? Diagnostic analysis indicates that while the output of one area was increased, that caused a bottleneck to occur at the next step in the manufacturing process which then flowed through and caused the overall output to decrease.
Predictive analytics. This answers the question of โWhat is going to happen?โ Predictive analytics uses historical data to forecast, or โpredict,โ potential future trends or outcomes.
A real-world example of predictive analytics is in the hospitality industry. Historical data analysis indicates peak demand for hotel rooms in New England occurs between October 8 and November 10 when tourists travel to the region to see the changing fall colors. Knowing this allows hotels to ensure they have plenty of heating oil, order sufficient food and supplies, and plan staffing accordingly.
Prescriptive analytics. This answers the question of โWhat should be done next?โ Prescriptive analytics is used by businesses to suggest possible future actions based upon probability-weighted projections that are typically generated by machine learning algorithms.
An example of this is how banks detect fraud. Algorithms are used to analyze historical data, as well as scan new data, and then compare them to determine if potential fraud (e.g., a stolen credit card) has taken place. If so, a recommendation is made to put the transaction on hold, contact the customer, verify if the transaction is valid or not, and then cancel the card and issue a new one.
Depending on the specific business situation, one or more of these approaches may be combined to provide the information a business may require.
An overwhelming amount of data
In todayโs world, data is the new currency, so understanding how to get the most out of that data is critical. However, so much data is generated every day, itโs hard to know where, or how to begin.
To put things in perspective and to provide an estimate of how much data is being created, in 2021, it was estimated there were 4.66 billion active users on the Internet every single day, creating an estimated 146,880 MB of data every day.1
By 2025 it is estimated there will be approximately 75 billion connected IoT devices in the world.2 And today, more than 2.5 quintillion bytes (thatโs 2.5 with seventeen zeroes after it) of data are generated every single day!3 Now granted, those numbers reflect the sum total of data created across the entire world, so the amount of data that businesses create is somewhat less, but this still provides an indication of how much data is being created.
Due to the extremely large amounts of data being created on a daily basis, this is where data analytics becomes valuable.
The value of data analytics
Businesses typically apply analytics to their data for the purposes of discovering insights, interpreting results, and identifying meaningful patterns within the data. This information is then used for a variety of purposes ranging from process improvement to new product development to informing business strategy.
Today, 94% of enterprises acknowledge that data analytics is important to their business growth and digital transformation, but 35% of companies with revenue in the $100-500M range and 54% of businesses with revenue under $10M have yet to make the change to becoming a data-driven business4.
However, a recent IDC study found that among organizations that have implemented data analytics tools and processes, better business outcomes are 2.5 times greater compared to other businesses that have not done so5.
A 2020 study by MicroStrategy found that in businesses that have embraced data analytics, significant benefits have resulted:
- Improved productivity and efficiency (64%)
- Faster, more effective decision making (56%)
- Better financial performance (51%)
- Identification and creation of new products and services (46%)
- Improved customer acquisition and retention (46%)
- Improved customer experiences (44%)
- Competitive advantage (43%)
This data supports the fact that the use of data analytics in business can provide a significant advantage. Data analytics can also drive improvements in other areas, such as:
- Reducing risk
- Improving employee engagement
- Increasing marketing and sales performance
Data analytics provides businesses with numerous benefits and advantages, which is one of the strongest reasons your business needs to implement them.
Why it applies to your business
As the business landscape continues to grow and change, new competitors, products and manufacturing methods enter the business world.
Keeping up with all the changes can be difficult, so taking advantage of the wealth of information contained in the data your business creates is critical. In addition to providing insights into how to run your business operations more effectively, effective use of data analytics also saves your business money.
Remember, as your business creates more data, data analytics helps you take full advantage of the information contained therein.
Explore how HPE Ezmeral can help build and accelerate your data analytics initiatives.
Richard Hatheway
Hewlett Packard Enterprise
twitter.com/HPE_Ezmeral
linkedin.com/showcase/hpe-ezmeral
hpe.com/software
[1] โ53 Important Statistics About How Much Data Is Created Every Day,โ Finances Online, https://financesonline.com/how-much-data-is-created-every-day/
[2]โHow Much Data Is Created Every Day?โ, https://seedscientific.com/how-much-data-is-created-every-day/ , SeedScientific, October 28, 2021
[3] โHow Much Data Is Created Every Day in 2022?โ, TechJury, November 26, 2022, https://techjury.net/blog/how-much-data-is-created-every-day/
[4] โGlobal State of Enterprise Analyticsโ, MicroStrategy, https://www3.microstrategy.com/getmedia/db67a6c7-0bc5-41fa-82a9-bb14ec6868d6/2020-Global-State-of-Enterprise-Analytics.pdf
[5] โHow Data Maturity and Product Analytics Improve Digital Experiences and Business Outcomes,โ https://assets.ctfassets.net/jicu8fwm4fvs/H4zSwtwM3wD4GeyoLsmit/38cf9bb379b001ef963e358be75dcfcc/IDC_HeapAnalytics_WhitePaper_Final.pdf by David Wallace, Research Director, Customer Intelligence and Analytics, IDC, 2022
RichardHatheway
Richard Hatheway is a technology industry veteran with more than 20 years of experience in multiple industries, including computers, oil and gas, energy, smart grid, cyber security, networking and telecommunications. At Hewlett Packard Enterprise, Richard focuses on GTM activities for HPE Ezmeral Software.
