There was a time when the juvenile startups used to go to the shipping centers for delivering their orders. During that time, the prediction of demand curves and the forecasting of business growth were relatively simple. It was not very difficult to predict and fulfill orders in a short span of time. Over a period of a few years, these juvenile startups have become established unicorns. These unicorns have hundreds of fulfillment centers around the globe and they deliver millions of shipping orders in a matter of hours. The question arises about the nature of forecasting which is not simpler anymore. This is because the demand-driven transactions in your favour are completely driven by data. Control over data and knowledge means full-fledged control over customers that serves as insurance for your business growth. Let’s understand this in deeper detail.
Logistics and the lake
Real-time processing of data can undoubtedly ensure us accurate and faster modes to predict growth and forecast future demand. The real-time processing of data helps us in efficient logistics and effective inventory control. That said, the logistics network has been constantly expanding for the last few years. The voluminous amounts of data make it necessary to conceive a data lake architecture that caters to this sudden overflow of information.
The internal expansion of data Lake has incentivized a large number of big companies to move their data into it. Being a central repository of information, a data Lake allows you to not only store but also discover structured information. In addition to this, data Lake also serves as a central repository of unstructured data. Without the involvement of large-scale data movement, different types of catalogs can be quickly scanned for periodic analysis. Even if the volumes of data continue to expand at full capacity, data lake ensures strict protection of data privacy. Data Lake can also be regarded as a link between data analytics and other types of machine learning tools.
The big data challenge
The challenges of big data range from data silos to difficulties in analyzing diverse data sets. One of the biggest problems for companies is that their data sets are segregated in various pockets. These pockets are controlled by different teams and to ensure migration to the data lake, all these challenges need to be overcome. This is where companies need to devise effective strategies for a smooth migration to the data lake. While different types of companies rely on manual data collection, there are other types of companies that rely on automation. It has been observed that the automation strategy is a better option when it comes to managing a voluminous amount of data.
To mine very fine details from segregated data sets is a difficult job to perform. With the help of data lakes, the analysis of such data would not only become easier but also accurate and accessible.