Data Lake for Enterprises

Data refers to a set of values of qualitative or quantitative variables.

- Wikipedia

Data can be broadly categorized into three types:

Structured data is data that we conventionally capture in a business application in the form of data residing in a relational database (relational database management system (RDBMS)) or non-relational database (NoSQL - originally referred to as non SQL).

Structured data can again be broadly categorized into two, namely raw and cleansed data. Data that is taken in as it is, without much cleansing or filtering, is called raw data. Data that is taken in with a lot of cleansing and filtering, catering to a particular analysis by business users, is called cleansed data.

All the other data, which doesn’t fall in the category of structured, can be called unstructured data. Data collected in the form of videos, images, and so on are examples of unstructured data.

There is a third category called semi-structured data, which has come into existence because of the Internet and is becoming more and more predominant with the evolution of social sites. The Wikipedia definition of semi-structured data is as follows:

Some of the examples of semi-structured data are the well-known data formats, namely JavaScript Object Notation (JSON) and Extensible Markup Language (XML).

The following figure (Figure 01) covers whatever we discussed on different types of data, in a pictorial fashion. Please don't get confused by seeing spreadsheets and text files in the structured section. This is because the data presented in the following figure is in the form of a record, which, indeed, qualifies it to be structured data:

Figure 01: Types of Data

Enterprise data refers to data shared by employees and their partners in an organization, across various departments and different locations, spread across different continents. This is data that is valuable to the enterprise, such as financial data, business data, employee personal data, and so on, and the enterprise spends considerable time and money to keep this data secure and clean in all aspects.

During all this, this so-called enterprise data passes the current state and becomes stale, or rather dead, and lives in some form of storage, which is hard to analyze and retrieve. This is where the significance of this data and having a single place to analyze it in order to discover various future business opportunities leads to the implementation of a Data lake.

Enterprise data falls into three major high-level categories, as detailed next:

The previously explained different types of enterprise data are very significant to the enterprise, because of which most enterprises have a process for the management of these types of data, commonly known as enterprise data management. This aspect is explained in more detail in the following section.

The following diagram shows the various enterprise data types available and how they interact with each other:

Figure 02: Different types of Enterprise Data

The preceding figure shows that master data is being utilized by both transaction and analytic data. Analytic data also depends on transaction data for deriving meaningful insights as needed by users who use these data for various clients.

- Wikipedia

- Wikipedia

As the preceding wikipedia definition clearly states, EDM is the process or strategy of determining how this enterprise data needs to be stored, where it has to be stored, and what technologies it has to use to store and retrieve this data in an enterprise. Being very valuable, this data has to be secured using the right controls and needs to be managed and owned in a defined fashion. It also defines how the data can be taken out to communicate with both internal and external applications alike. Furthermore, the policies and processes around the data exchange have to be well defined.

Looking at the previous paragraph, it seems that it is very easy to have EDM in place for an enterprise, but in reality, it is very difficult. In an enterprise, there are multiple departments, and each department churns out data; based on the significance of these departments, the data churned would also be very relevant to the organization as a whole. Because of the distinction and data relevance, the owner of each data in EDM has different interests, causing conflicts and thus creating problems in the enterprise. This calls for various policies and procedures along with ownership of each data in EDM.

In the context of this book, learning about enterprise data, enterprise data management, and issues around maintaining an EDM are quite significant. This is the reason why it's good to know these aspects at the start of the book itself. In the following sections we will discuss big data concepts and ways in which big data can be incorporated into enterprise data management and extend its capabilities with opportunities that could not be imagined without big data technologies.

Let me start this section by giving the Wikipedia definition for Big Data:

- Wikipedia

Let's try explaining, the two sentences that are given in the preceding Wikipedia definition. Earlier, big data referred to any data that is large and complex in nature. There isn't any specified size of data for it to be called big data. This data was considered so big that conventional data processing applications found it difficult to use it in a meaningful fashion. In the last decade or so, many technologies have evolved in this space in order to analyze such big data in the enterprise. Nowadays, the term big data is used to refer to any sort of analysis method that can comprehend and extract this complex data and make valuable use of it in the enterprise.

Big data and 4Vs

Whenever you encountered the term big data being overly used, you must have come across an important aspect with regard to it, called 4Vs (until recently, it was 3Vs, and then the fourth, very significant, V got introduced). The 4Vs, namely variety, velocity, volume, and veracity (in no particular order) determine whether the data we call Big Data really qualifies to be called big:

• Variety: In the context of big data, variety has a very important place. Variety refers to vivid types of data and the myriad sources from which these are arrived at. With the proliferation of technologies and the ever-growing number of applications (enterprise and different personal ones), there is high emphasis on data variety. This is not going to come down any time soon; rather, this is set to increase over a period of time, for sure. Broadly, data types can be categorized into structured and unstructured. Applications during this time deal mainly with structured data stored mostly in a relational database management system (RDBMS). This is very common, but nowadays, there has been the need to look at more unstructured data, and some of the examples can be video content, image content, file content in the form of binaries, and so on.
• Velocity: In the context of big data, velocity is referred to in two aspects. First is the rate at which the data is generated, and second is the capability by which the enormous amount of data can be analyzed in real time to derive some meaningful conclusions. As the proverb goes, Time is money, this V is a very important aspect, which makes it easy to take quick decisions in real time. This aspect is one of the strongholds of some of the businesses, especially retail. Giving the customer a personalized and timely offer can be the deciding factor of the customer buying a product from you or ditching you to select a more favorable one.
• Volume: In the context of big data, volume refers to the amount/scale of data that needs to be analyzed for a meaningful result to be derived. There isn't a quantitative figure that categorizes a data to be falling into big data. But usually, this volume is definitely more than what a conventional application is handling as of now. So, in general, this is quite big and does pose a problem for a traditional application to deal with in a day-to-day fashion (OLTP - OnLine Transaction Processing). For many businesses, analyzing and making use of social data has become a necessity. These social apps (Facebook, Google+, LinkedIn, and so on) have billions of registered users producing billions of data (structured and unstructured) in a day-to-day fashion. In addition to this, there are applications that themselves produce a huge amount of data in the form of conventional transactions and other analytics (behavioral, location-based, and so on). Also, with the growing number of wearables and sensors that emit data every millisecond, the volume aspect is going to be very important, and this is not going to come down any time soon.

As detailed in the previous section, until recently, there used to be 3Vs. But quite recently, the fourth V was introduced by IBM, namely veracity. For data growing at an exponential rate and as deduced from different reliable and unreliable sources, the significance of this V is huge.

You must have already heard/read of fake news/material being circulated in various social media when there is something important happening in the world. This V brings this a very important aspect of accuracy in big data. With proliferation of data, especially in social channels, this V is going to be very important, and rather than 3Vs, it is leaning highly towards 4Vs of Big Data.

• Veracity: In the context of big data, veracity refers to accuracy of data being analyzed to get to a meaningful result. With a variety of sources, especially the not-so-reliable user-entered unstructured data, the data coming from some of these channels has to be consumed in a judicial manner. If an enterprise wants to use this data to generate business, its authenticity has to be verified to an even greater extent.

Big Data and its significant 4V's are shown in a pictorial representation, as follows:

Figure 03: 4V's of Big Data

Figure 03 clearly shows what the 4V's are and what each of these V's means, with adequate bullet points for easy understanding.

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:

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.

There is no doubt that high-quality data (cleansed data) is an irresistible asset to an organization. But in the same way, bad quality or mediocre quality data, if used to make decisions for an enterprise, cannot only be bad for your enterprise but can also tarnish the brand value of your enterprise, which is very hard to get back. The data, in general, becomes not so usable if it is inconsistent, duplicate, ambiguous, and incomplete. Business users wouldn't consider using these data if they do not have a pleasant experience while using these data for various analyzes. That's when we realize the importance of the fourth V, namely veracity.

Quality of data is an assessment of data to ascertain its fit for the purpose in a given context, where it is going to be used. There are various characteristics based on which data quality can be ascertained. Some of which, not in any particular order, are as follows:

There are characteristics/dimensions other than what have been described in the preceding section, which can also determine the quality of data. But this is just detailed in the right amount here so that at least you have this concept clear in the head; these will become clearer as you go through the next chapters in this book.

The data in an enterprise lives in different formats in the form of raw data, binaries (images and videos), and so on, and in different application's persistent storage internally within an organization or externally in a private or public cloud. Let's first classify these different types of data. One way to categorize where the data lives is as follows:

Another way in which data living in an enterprise can be categorized is in the form of different formats in which they exist, as follows:

Now let's get into a bit more detail about these different data categories.

As explained briefly in the previous sections, the current state of enterprise data in an organization can be summarized in bullets points as follows:

We wouldn't say that creating or investing in Data lake is a silver bullet to solve all the aforementioned deficiencies. But it is definitely a step in the right direction, and every enterprise should at least spend some time discussing whether this is indeed required, and if it is a yes, don't deliberate over it too much and take the next step in the path of implementation.

Data lake is an enterprise initiative, and when built, it has to be with the consent of all the stakeholders, and it should have buy-ins from the top executives. It can definitely find ways to improve processes by which enterprises do business. It can help the higher management know more about their business and can increase the success rate of the decision-making process.

- infoworld.com

Digital transformation (DX) is an industry buzzword and a very strong initiative that every enterprise is taking without much deliberation. As the word suggests, it refers to transforming enterprises with information technology as one of its core pillars. Investing in technologies would definitely happen as part of this initiative, but data is one of the key aspects in achieving the so-called transformation.

Enterprises has known the importance of data and its analysis more and more in recent times, and that has definitely made every enterprise think out-of-the-box; this initiative is a way to establish data at the center.

As part of this business transformation, enterprises should definitely have Data lake as one of the core investments, with every department agreeing to share their data to flow into this Data lake, without much prejudice or pride.

We saw the importance of data in an enterprise. What enterprises face today is how to mine this data for information that can be used in favor of the business.

Even if we are able to bring this data into one place somehow, it's quite difficult to deal with this huge quantity of data and that too in a reasonable time. This is when the significance of Data lake comes into the picture. The next chapter details, in a holistic fashion, what Data lake is. Before getting there, let's detail the use case that we are trying to achieve throughout this book, with Data lake taking the center stage.

Data lake implementation using modern technologies would bring in many benefits, some of which are given as follows:

The use case that we will be covering throughout this book is called Single Customer View. Single Customer View (SCV) is a well-known term in the industry, and so it has quite a few definitions, one of which is as follows:

- Wikipedia

Enterprises keeps customer data in varying degrees siloed in different business applications. The use case aims at collating these varying degrees of data from these business applications into one and helping the analysts looking at this data create a single customer view with all the distinct data collected. This single view brings in the capability of segmenting customers and helping the business to target the right customers with the right content.

The significance of this use case for the enterprise can be narrowed down to points as listed next:

Conceptually, the following figure (Figure 05) summarizes the use case that we plan to implement throughout this book. Structured, semi-structured, and unstructured data is fed into the Data lake. From the Data lake, the Single Customer View (SCV) is derived in a holistic fashion. The various data examples are also depicted in each category, which we will implement in this book. Doing so gives a full use of a Data lake in an enterprise and is more realistic:

Figure 05: Conceptual view of Data lake use case for SCV

Figure 05 shows that our Data lake acquires data from various sources (variety), has different velocities and volumes. This is more a conceptual high-level view of what we will be achieving after going through the whole book.

We are really excited, and we hope you are, too!

In this chapter, we delved deep into some of the common terminologies in the industry. We started the chapter by understanding data, enterprise data, and all important big data. We then dealt with the relevance of data, including various data quality attributes. After that, we went into the details of the different types of data classification and where the data lives in an enterprise.

In the sections that followed, we dealt with digital transformation and finished the chapter with the use case that we will be implementing throughout this book in more detail.

After completing this chapter, you will have clearly grasped many terminologies and will also have a good understanding of the significance of data and Data lake to an enterprise. Along with that, you will have a very good idea about the use case and its relevance.

In the next chapter, we will delve deep into Data lake and also the pattern that we will use in implementing Data lake in more detail.