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The Difference Between Big Data and Data Analytics
Moto GP Bikes transmit 2GB of data every lap and 3TB during a full race. When you consider there are 20 races per season, the bikes are generating over 60 terabytes of data.
While that is a huge amount of data, the race-time decisions are not driven by the amount of data, but rather by questions that need answers. This is the fundamental difference between Big Data and Data Analytics.
- What is Big Data? It’s defined as high-volume, high-velocity and/or high-variety information assets (data) that require innovative forms of information processing to collect, clean, store and use.
- What is Data Analytics? It’s the process of examining data with a specific objective in mind to find answers that help you make evidence-backed business decisions.
The 60TB of data collected during a Moto GP season is big data. It’s a huge volume of data that consists of different types of data(variety), both structured and unstructured. Analysts and data scientists must process this type of data using powerful computing systems and then sift through it to discover trends or correlations that can help solve problems.
This type of exploratory big data analytics usually leads to new, macro-level efficiencies for your business. For example, the MotoGP team might discover by analysing their big data that they have slower lap times when they use a certain type of fuel. By changing the fuel, they might give themselves a better chance of winning.
Data analytics, on the other hand, is designed to help you answer questions around specific business objectives. In the same way a MotoGP team wants to know which lap is best for a pit stop, data analytics helps you answer questions that help your business win.
Data Analytics is Transforming Business
Believe it or not, your company creates just as much data as a MotoGP team, if not more. Every operational task and customer interaction generates valuable data that you can analyse to make better decisions and uncover insights with the power to transform your business.
The companies that have embraced data analytics not only outperform their competitors, but they continually exceed expectations.
According to the report, companies that use data analytics to make decisions across their business have a 23x greater likelihood of customer acquisition, a 6x greater likelihood of reducing customer churn, and a 19x greater likelihood of being profitable.
Netflix uses data analytics to select what types of content they produce. By analysing the behavioural data of what their viewers watch, they can develop content that will appeal to those behaviours. The result: Netflix sales are up 36% and they produced 5 of the top 10 shows people searched for in 2016.
The potential of your data goes beyond just making good decisions. You have the ability to find insights about your business or customers that can reshape your approach and lead to tremendous growth.
Data Analytics – Four Step Process
Step 1: Set clear objectives. Data analytics should always start with a business objective. Are you trying to reduce fuel costs like UPS or reduce customer churn like T-Mobile? Once you establish your goals, it will be much easier to figure out your questions and the data you will need to find answers.
Step 2: Identify the right data. When you know what you want to achieve, the next step is to find the best data and metrics within your technology applications. For example, if you want to track your customer experience across social and your site, you could use an identity management tool like Auth0 to collect your customers’ social media data.
Step 3: Use a tool to analyse data. In the past, you needed a data analyst who was highly trained in statistics. Technology has changed the game, giving every business of every size the ability to get answers from their data. If you do not have an IT team to help you, there are several self-service analytics tools available, that are designed to use your data to answer your questions.
Step 4: Make the decision. Data analysis gives you the information necessary to make a decision or take an action, but it can’t make the decisions for you — at least not yet. Your ability to interpret the data and act on the insights is what truly sets you apart from the competition.
To help you interpret your analysis, ask yourself these three questions:
- How does the conclusion answer the question?
- Does the data help defend against any objections? How?
- Can the insights be used to make tangible changes to the business?
Technology is making it possible for every business to collect and analyse data without the need for data scientists or even IT. At the end of the day, it is not your lack of data or analytics tools that will make the difference. It is your ability to set clear objectives, ask the right questions and act on the insights from the analysis.
Data Analytics is Worthless Without You
A lot of hype is built around data and its potential to improve business or even change the world. The reality is that data is worthless until it is processed and analysed to answer questions.
Business moves at an incredibly fast speed. Like a MotoGP team, your company can take a considerable lead if you know what questions of your data need answering and how you will take action once you have the answers.
Every business has access to data. The competitive advantage will be realized by the companies that can turn their data-driven insights into decisions and ideas that transform the business and drive growth.
How Data Analytics Helps Small Businesses Discover New Insights
SMBs and non-profits mine data to improve their businesses and missions.
Small Businesses Start Thinking Analytically
Not many small or medium-sized businesses or non-profits need the data analytics firepower, but most would benefit from the increased efficiency and productivity data analytics tools offer, Adoption of analytics tools, however, lags behind their promise, because SMB tech staffs may not be familiar with the technologies or require training to use them effectively.
The most common use of data analytics in midsized companies is monitoring IT functions, with some inroads in finance and operations. Most deployments are at the department level rather than companywide.
IDC predicts more widespread use of analytic applications as they increasingly become available from the cloud. Software manufacturers are also starting to present a more sophisticated value proposition for data analytics to SMBs, stressing the potential of these tools in areas such as workforce, management and sales force analytics.
Data analytics have to be part of the culture for organizations to get full value from the technologies. “You can take it as far as you want to go into the data you gather as a company, but right now SMBs are not generally taking it very far yet.”
The Future of Business Intelligence – What’s Coming in 2018 and Beyond
The future is coming… fast! At least when you’re talking about advances in technology, it is. Business Intelligence (BI) is no exception. The introduction of BI wasn’t all that long ago for most of us, and its evolution is proceeding at a frantic pace. The rapid advancements are, in large part, due to the immense value BI provides. As game-changing use cases and success stories continue to emerge, so do new ideas for enhancements, innovations and next-level analytic functions.
- Self-Service BI
- Data Discovery & Visualizations
- Automation & Augmented Analytics
- Data Quality
To know how our organization EzDataMunch can help you transform your business,
please visit: https://ezdatamunch.com/industry-solutions/
Client Partner | EzDataMunch
Prashant is working as a Client Partner at EzDataMunch – as a liaison between clients and management for business executions, Understanding client needs and identify new business opportunities, Negotiate business contracts and costs with customers as needed, Develop customized programs to meet client needs and close business, Provide client consultations about company products or services, Develop business proposals and make product presentations for clients, Build positive and productive relationships with clients, Understanding market trends for BI product.