Yesterday I met up with my friend, who, for want of a better word, is a connoisseur of cutlery. (Don’t worry—he’s not dangerous.) He demonstrated in great detail how paring knives work best for peeling apples or oranges, while a serrated edge knife for cutting tomatoes, and a chef’s knife for slicing vegetables.
It got me to thinking about how in the analytics world, we often say that we “slice and dice data.” Then I realized that the metaphor of different knives applied as well. When we want to validate different hypotheses, we may use a different data analysis method, or possibly a different data store. For instance, “search and retrieval” works well with a graph database, while document data stores are a better fit for analytical processing. These are like my paring knife and serrated knife.
So, to extend the metaphor further—the best meals include many different types of foods, and therefore require many knives in the preparation. So it goes with data analytics. If I want a full meal of insights then I will need many different tools. But such a collection of data analytics tools is not as easy to acquire as a set of kitchen knives. When you consider the fact that the value of insight is bound by time, the cost of gaining access to and setting up different technologies can be quite prohibitive.
That’s what brings us to cloud computing, the channel that best enables one’s ability to harness different technologies while being economical in operational spend. The investment can be further optimized through the automation that’s possible in cloud solutions.
Data: Currency in the Cloud
Data is the new currency of the global economy. But its value is only realized when it can be stored in polyglot databases and analyzed using various tools. This data is not only collected from internal systems, but also externally sourced (especially for better personalization initiatives). Additionally, internal systems could be legacy systems of record or SaaS-based systems. This data store is what we refer to as data lake or reservoir.
Once a data lake is set up, sharing of data with different internal teams and partners is the next key step. So the question is where everything should be placed. I would argue that the cloud is the best fit, for two main reasons:
- Source data must be close to analytics and hence it makes more sense to have the data in the cloud.
- Data sharing and governance can be better enforced in the cloud due to its software defined nature.
The true value of this data is finally harnessed through machine learning and deep learning algorithms that are applied to the data lake. The fact that most of these technologies have followed a PaaS-based route only strengthens the argument of placing the data in the cloud.
Decision Moments: Your Complete Cutlery Set for Analytics
Getting back to the idea of needing a suite of data analytics tools in order to properly slice and dice data for all the needs across an organization, I am proud to be a part of Mindtree’s newest launch: Decision Moments. It is a cloud-based data analytics platform that applies continuous learning algorithms to large data pools, allowing businesses to generate meaningful and compelling insights that improve over time.
Full details are available here, but the quick overview is that Decision Moments takes the complexity out of big data while allowing businesses to unlock the value hidden in available data pools via an intelligent, fast, easy-to-use platform.
Like a Japanese Hibachi chef, we’re eager to show off how well it slices and dices. It may not have the same entertainment value of flying knives and big flames, but I promise the business value will go well beyond that of a good meal.