October 2, 2020 | Divyansh Meena
Data is the new oil, and it’s real power comes from harnessing it to drive business innovation. But it is not that easy to be a data powered enterprise. According to IDC, these are the top three data challenges that are faced by enterprises:
Data volume is expected to go up from 33ZB in 2018 to 175ZB by 2025, that is a 530% increase. This increased data volume will skyrocket the data storage cost making this is an inevitable but expensive affair.
The size of the data alone is not the challenge but the nature of that data it is what will be difficult to manage. By 2025, more than 25% data will be streaming data. Real-time data is way more difficult to manage as compared to historical data.
The majority of data today is unstructured and only 1% of it is analyzed. With the amount of data growing by 5X in the next 5 years, it is going to become overwhelming to build an infrastructure that can drive maximum intelligence from the data.
Every lock has a key and the hope is not lost for enterprises. We at Pluto7, being awarded as Partner of the Year for Data and Analytics 2019 by Google Cloud , firmly believe that this five step structure built by Google Cloud can help a business become a data powered enterprise. These five steps are applicable to an enterprise irrespective of the vertical they are in, be it gaming or retail.
The traditional data warehouse implementations often hinder the business transformation since they are not easily scalable and increase the operational cost significantly. The first step is to simplify the operations and cut down the ownership cost to utilize the monetary resource for innovation. With Google Cloud, an enterprise can cut down its TCO easily by 50%.
Every department in a given enterprise has functions that influence one another but have unrelated data creating data silos. Think about different related departments like sales and marketing, their representative data silos adding to myriads of data looks structured but is scattered from an analytics point of view. In this step, the enterprise works to democratize data by breaking data silos and building a data culture while making sure that compliance terms are followed.
Location is a critical aspect for making contextual decisions. The streaming data enables faster decision making, by default all the SQL query in Google Cloud is streaming. It is important to make sure that the data is ingested, transformed/enriched and is streaming in the data warehouse. Stream data analytics on GCP enables real-time decision making.
This step onwards the enterprises can start harnessing the intelligence from their enriched data. Now, you can start generating prescriptive analytics. Rather than building those AI solutions in house you can always reach out to partners like Pluto7. Lot of APIs are already built by Google for enabling use cases like video classification, object tracking and a lot more.
Every business has its own set of challenges that can be solved with a set of use cases, few of them have to be custom-built and others can be bought as plug-and-play elements. While customizing AI-driven ML models consider these three criteria: how fast do you want it? ; How much effort would you want to invest? How much differentiation does this business use case have to offer? Answering these questions will help you prioritize and decide what you can build in house and what has to be outsourced.
Whether you are building an ML model on your own or purchasing from outside, the initial steps always have to be around building a robust data management system. At Pluto7 we are already helping fortune 500 enterprises democratizing their data and then generating valuable insights for their respective businesses.
Here is how our team is helping a leading data storage solutions provider in optimizing demand forecasting process: Link
LNutra is gaining a 360° view of their customer journey & activating marketing data like never before: Link
And the list goes on. If you want to know more about our customer success stories or if you are looking for some help on your data front, feel free to write us at firstname.lastname@example.org