Relevance of data

To any enterprise, data is very important. Enterprises have been collecting a good amount of past data and keeping it in a data warehouse for analysis. This proves the importance of data for enterprises for past data analysis and using this for future enterprise growth. In the last decade or so, with the proliferation of social media and myriads of applications (internal to the enterprise and external cloud offerings), the data collected has grown exponentially. This data is increasing in amount as the day goes by, but enterprises are finding it really difficult to make use of these high volumes of diverse data in an effective manner. Data relevance is at the highest for enterprises nowadays as they are now trying to make use of this collected data to transform or energize their existing business.

A business user when fed with these huge amounts of data and right tools can derive real good value. For example, if customer-related data from various applications flows into a place where this data can be analyzed, this data could give a good amount of valuable insights, such as who is the customer who engages with various website pages of the enterprise and how. These derivations can be used as a way in which they can look at either changing their existing business model or tweaking certain business processes to derive maximum profit for the enterprise. For example, looking at various insights from centralized customer data, a new business model can be thought through, say in the form of starting to look at giving loyalty points to such customers. This data can also be made use of, giving more personalized offers closer to customer recommendations. For example, looking at the customer behavior, rather than giving a general offer, more personalized offers suiting the customer's needs could be offered. However, these are fully dependent on the business, and there isn't one approach fitting all the scenarios. These data can, however, be transformed and cleansed to make them more usable for a business user through different data visualizations techniques available as of now in the form of different types of graphs and charts.

Data is relevant, but where exactly this data lives in an enterprise is detailed in the following section.

Vit Soupal (Head of Big Data, Deutsche Telekom AG) in one of his blogs defines these 4V’s of big data as technical parameters and defines another three V’s bringing in business into context. We thought that we would not cover these additional V’s in our book, but these are definitely required for Data lake (Big Data) to be successful in an enterprise.

These additional 3 Vs (business parameters) are as follows:

• Vision: Every enterprise embarking on Big Data (Data lake) should have a well-defined vision and should also be ready to transform processes to make full use of it. Also, management in the enterprise should fully understand this and should be in a position to make decisions considering its merits.
• Visualization: Data lake is expected to have a huge amount of data. Some will make a lot of sense and some won't at certain points in time. Data scientists work on these data and derive meaningful deductions, and these need to be communicated in an effective manner to the management. For Big Data to be successful, visualization of meaningful data in various formats is required and mandated.
• Value: Big Data should be of value to the enterprise. These values could bring about changes in business processes or bring in new innovative solutions (say IoT) and entirely transform the business model.

Vit also gives a very good way of representing these 7 V’s as shown in the following figure:

Figure 04: 7 V's of big data

Figure 04 shows that Big Data becomes successful in an enterprise only if both business and technical attributes are met. 

The preceding figure (Figure 04) conveys that Data lake needs to have a well-defined vision and then a different variety of data flows with different velocity and volume into the lake. The data coming into the lake has different quality attributes (veracity). The data in Data lake requires various kinds of visualization to be really useful to various departments and higher management. These useful visualizations will derive various value to the organization and would also help in making various decisions helpful to the enterprise. Technical attributes in a Data lake are needed for sure (variety, velocity, volume, and veracity), but business attributes/parameters are very much required (vision, visualization, and value), and these make Data lake a success in the enterprise.