Modern Businesses are built on data, but a traditional approach to this data isn’t sufficient to survive in the highly competitive business world of today. More and more firms are adopting complex models for acquiring and processing data. Companies are restructuring to incorporate new sections for data science within their business models and strategies.
This incorporation allows companies to keep up with the flow of Big Data, which involves massive inflows of unstructured and structured data that is seemingly disconnected. The proper use of this valuable data would allow firms to derive customer insights, identify opportunities for improving productivity and efficiency, highlight changing trends, and guide the formation of actionable plans. Successful Big Data incorporation has a direct impact on value creation. This includes:
Consumer insights derived from big data would allow companies to identify trends, tastes, and behaviors, which can be used as a base for thorough and targeted strategies. It can also help in identifying the customer journey, and how to ensure that they finish the whole journey successfully. All of this would ultimately increase customer retention, thereby boosting business values in short order.
Data analytics and processes can help firms through identifying leads, filtering solid prospects, and pinpointing market opportunities. The insights derived can help firms make the necessary adjustments to optimize their pricing strategies and marketing campaigns, and find opportunities to automate sales’ processes.
Firms can also use big data analytics to pinpoint problems in management processes and production that hinder productivity and efficiency. For example, machine-generated and business generated data can be used to identify opportunities for improving the supply chain and material procurement processes.
The next step for companies would be to ensure that big data analytics are an automated part within the business structure. It would guarantee that they benefit from real-time big data to make timely and effective decisions and adjustments. There are 4 main aspects of real-time big data automation that are necessary for firms to make the most out of this opportunity:
An automated data management system allows the firm to have a self-regulating model that processes the various data maintenance requirements, such as, cataloging, security, governance, and integration both in batch and real-time. It creates positive value by decreasing data maintenance time requirements while increasing data unification. It is also vital to offer solid data protection to lower the risk of a data breach, allow for faster and easier data retrieval and shorten time-to-value, and provide a unified view of data.
Data preparation can be time-consuming, inhibiting data analysts from focusing on other important tasks. Fully or semi-automating the process will not only free up data analysts for higher-value work but would also allow for a shorter time-to-value and improved predictive performance. Automating the process would include automating data cleanup through primary analysis, data discovery, feature removal, feature engineering, and variable transformation.
Data pipelines are automated processes for extracting, reformatting, aggregating, migrating, and preparing big data for analytics. It results in fewer human errors, faster results, faster insights, and fewer man hours.
Even with a set model or project in motion, the need to make changes and adjustments are expected. Tracking changes and tweaks while a model is in-production, with the use of an automated model management tool, will be useful for saving the state of the firm’s environment, configuration, statistics, files, and code. There is also the option of doing this in a cloud environment or on-premise.
Firms cannot afford to ignore the importance of data science or big data when competing on a local or global scale. It is a game changer for productivity, efficiency and leadership in the market. Get in touch with our consultants now, and find out how your company can incorporate real-time big data into its business structure on: