An enterprise is almost always filled with data. This includes things like laboratory notebooks, news reports, structured databases, and technical reports, among many others. And every single year, relevant data volume goes up.
The problem is most data is uncategorized. This makes it inaccessible. When the data is accessible, it is usually not indexed so even if you are authorized to access it, you will find it difficult to locate what you are looking for. Curating data requires a pretty complicated approach. This is costly and the business who does not have the necessary programs cannot take advantage of the information offered by the data.
Enterprise Data Science
Enterprise data science started to be used as a term to describe the digital transformation the company goes through in order to properly utilize the data it has access to. This is more than big data and takes advantage of modern cloud computing and machine learning. The goal is to extract important knowledge from the enterprise’s digital assets so that it can help in change and the creation of value for several processes.
Regular data science only uses statistics and machine learning. Enterprise data science is more complicated and aims to maximize what the digital assets offer.
Why Is Enterprise Data Science Important?
Enterprise data science offers several benefits that have to be taken into account by the decision-makers. This ranges from identifying brand new business and manufacturing opportunities to increasing operational efficiencies. Simply put, the use of enterprise data science automatically accelerates knowledge discovery. It also facilitates knowledge diffusion all across the company. We are thus looking at important valuable value creation.
To better highlight the benefits, we have to highlight that most enterprises these days have a problem with the supply chain. But, we are talking about the information data chain. As opposed to smaller companies, enterprises have several information sources available, not a few ERP systems. Expense records can easily become imprecise, inaccurate, and incomplete. The exact same thing can happen with several data types.
When dealing with information, enterprise data science is simply very important. This is because:
- The DDL module becomes an information acquisition module (cloud-based) that is responsible to load data and connect data sources.
- The DE module becomes a data processing framework. It allows the user to enrich, analyze, summarize, and synthesize data. This is much more than just data augmentation since it includes several data quality elements, like derivative reference schemes, consistency schemes, and structure schemes.
- KB modules become distilled knowledge coming from several data sources that were evaluated, integrated, and then used for creating value and analysis.
- The module used for analytics offers extra information through several possible interfaces. It simply becomes a lot easier to access the gathered data to then analyze.
At the end of the day, enterprise data science became a necessity, one that large companies simply cannot neglect. Due to the need to work with a lot of data, companies that do not invest in the creation of this systems will lose money.