Often without realizing, companies gather large quantities of data over time, simply by doing their day to day activities. The type of data acquired depends on the activities themselves, as well as the type of company. Although data is not necessarily information, it can be translated as such. Information is in essence data that is usable. Generally speaking this is structured data, as it is already processed. Traditionally this is data that can be found in a database, following a clearly defined and organized structure. Most data doesn’t follow this structured setup, is referred to as unstructured data and comprises more than 80% of enterprise data. This type of data consists of, but is not limited to: natural language, pictures, videos, web server logs, data recorded by data-capturing devices such as GPS-trackers but also blog entries such as this one.
All these different kinds of data on their own are very hard to analyze, especially in greater quantities. Although in the past this data was quite impossible to handle, with the advancement of technologies there are more efficient ways to analyze this data than by doing it manually. Pattern recognition, cognitive analytics and artificial intelligence make this process of analyzing the data and transforming it into actual information a lot easier. In order for data analysis and analytics to be successful and offer value, enterprises need to have proper data management and big data governance frameworks.
A lot of data is already publicly available, but when pairing it with private data that companies own, this can be used for more specific insights. Depending on the data, it can be used for many different situations. For instance, texts from social media messages and natural language (such as audio from phone calls) could be used to determine what a (prospective) client thinks of a product or company. If consumers are unhappy with specific aspects of a product, the company that makes that product could improve on those. This can then be used to lower the churn rate, or in other words increase customer retention. Likewise, it could also be used to acquire new customers, or increase the return on marketing investments by using predictive analytics and targeted promotions. Other data such as web server logs and data captured by devices can be utilized to detect fraud.
Although these are just some examples of how data can be used, each use on its own can only improve a company so much. When utilizing different uses, the data can help with forming a company’s strategy. With the ever-growing amount of data, these insights will lead to more accurate predictions. These can lead to data-driven decision making, moving the strategy away from an intuitive approach, reducing the effect human errors have on the decision making.
Hi,
I think it was an interesting post!
What I wonder though is do firms still realy don’t know that they own this “treasure cove”?
I mean about 10 years ago this might have been the case but today? Big data is basically always in the news and one of the most heard things about this is that firms don’t realize the value of what they own, so how would it be possible for a firm to still don’t know this?
This doesn’t mean that these firms know what to do with it though so it’s still a really imporant topic!
Thanks for sharing, and I would like to share some practical experiences on big data in my country.
Big data has really become big recently. Technologies such as machine learning, text mining, and image recognition are also very popular in terms of methods for data analysis. However, in practical, many big firms respond slow to the trend of big data even though these firms usually have a lot of data. In my country, only very few of big firms are making good use of the advantage. When I worked in one of the big banks in my country, my boss even told me that the bank was a treasure cove of data, but unfortunately it was hard to access the data. Therefore, the problem is not the technology. The issue is that firms don’t realize what they have. And even though they know what they have, they don’t know how to make the data useful.