Data, data, and data – you hear so much about it, but do you really understand the importance of data? At its most basic, data is simply a collection of different facts, including numbers, measurements, and observations, that have been translated into a form that computers can process.
In this digital age, we’re dependent on data. It plays a significant role in nearly every second of our lives — profoundly more than the days of W. Edwards Deming, who coined the quote of the title.
This might sound easy, but data is effectively changing the world we live in and the way that we work. If you own a business and are looking to grow, you likely have some notion that data is crucial in helping you take the next step.
Data Analysis
Being a data-driven business is important, but what does that mean exactly?
As technology continues to improve and more professionals are educated in data, we will see more companies entering the data-driven realm.
The term data-driven describes a business state where data is used to power decision-making and other related activities efficiently, in real-time. For a business, reaching the data-driven state is like the difference between driving an automobile and traveling by horse. Data-driven businesses get to their destination faster and more efficiently. Data-driven characteristics include well-integrated data of good quality and algorithmic automation, including artificial intelligence (AI).
So what is data analysis?
If your business is not growing, then you have to look back and acknowledge your mistakes and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business to grow more. All you need to do is analyze your business data and business processes.
In simple words, data analysis is the process of collecting and organizing data in order to draw helpful conclusions from it. The process of data analysis uses analytical and logical reasoning to gain information from the data.
Businesses use data analysis, which help to make better business decisions. Whether it is market research, product research, positioning, customer reviews, sentiment analysis, or any other issue for which data exists, analyzing data will provide insights that organizations need in order to make the right choices.
Benefits of data analysis
As the importance of data analysis in the business world increases, it becomes more critical that your company understand how to implement it. Some benefits of data analysis include:
- Ability to make faster, more informed business decisions, backed up by facts.
- Deeper understanding of customer requirements which, in turn, builds better business relationships.
- Increased awareness of risk, enabling the implementation of preventative measures.
- Improved flexibility and greater capability in order to react to change – both within the business and the market.
- Better insight into the financial performance of the business.
- Proven to reduce costs and therefore increase profit.
When Does Data Become Big Data?
Big Data is also data but with a huge size. Big Data is a collection of data that is huge in volume and yet growing exponentially with time. In short such data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently.
Systems, that process and store big data have become a common component of data management architectures in organization. Big data is often characterized by the 3Vs: the large volume of data in many environments, the wide variety of data types stored in big data systems and the velocity at which the data is generated, collected and processed.
With Software as a Service (SaaS) and cloud computing becoming increasingly popular, storing data in large quantities became easier and very important.
Some statistics
- The big data industry will be worth an estimated $77 billion by 2023. (Entrepreneur, 2019)
- More than 150 zettabytes (150 trillion gigabytes) of data will need analysis by 2025. (Forbes, 2019)
- The big data industry will be worth an estimated $77 billion by 2023. (Entrepreneur, 2019)
- Businesses that use big data saw a profit increase of 8–10 percent. (Entrepreneur, 2019)
Big Data Challenges
While big data holds a lot of promise, it is not without its challenges.
First, big data is…big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.
But it’s not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that’s relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.
Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.
Examples of Big Data
Here are some examples of big data:
- The New York Stock Exchange generates about one terabyte of new trade data per day.
- The statistic shows that 500+terabytes of new data get ingested into the databases of social media site Facebook, every day. This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc.
- A single Jet engine can generate 10+terabytes of data in 30 minutes of flight time. With many thousand flights per day, generation of data reaches up to many Petabytes.
Inventory Management in the Era of Big Data
Before the advent of cloud solutions and the availability of big data, collecting analytical information about inventory required more manpower and resources. Not to mention the high degree of errors that arose due to manual entry into excel sheets. With the coming of big data in inventory management, its associated operations have also become more streamlined than ever. However, too much data without the proper technology, infrastructure, and qualified personnel to handle it, will not serve its purpose.
