Those companies that use Big Data collected in their business systems to improve operations can offer better service to customers, make enhanced personalized marketing or advertising campaigns based on their target market preferences, and over time boost profitability. Those businesses that use Big Data possess a competitive advantage over those companies that do not use Big Data to make better and faster decisions for their businesses, subject to the wise use of this data.
For instance, Big Data offers companies valuable insights into customers that can be deployed for refining marketing campaigns and techniques for boosting conversion rates and customer engagement. Every business has data and to use it correctly you need to know how to go about it. Thus you can make the best use of it, getting the perfect results for your business.
Besides the above, deploying Big Data helps companies to be customer-centric. The real-time and the historical data collected can be deployed for accessing the evolving personal preferences of the customer to help the business update and boost their strategies for marketing to become more responsive to the needs and desires of the customer.
Though Big Data seems profitable for modern businesses today, another question that owners of organizations ask themselves when it comes to the question of Big Data architecture- what are its major challenges, and can experience IT, managers in the organization effectively resolve them?
Understanding the key hindrances to effective Big Data architecture
Big data faces issues regarding costs and its capacity for processing; however, they are not the only challenges that DBAs face. The design of Big Data architecture is a general challenge as well. Most users face a lot of hurdles when it comes to the above task. The systems of Big Data should be customized to suit the specific needs of the company. This is where an undertaking should be considered for the task with a team of experienced IT experts and skilled application developers to come up with a feasible tool derived from all the latest available technologies.
The organization should have a fresh skill
When it comes to the deployment and management of systems revolving around Big Data, there is a need for fresh remote DBA skills and those possessed by existing database administrations and developers who focus on relationship software. Both of these issues can be effectively resolved by deploying a managed cloud service in the above case. However, IT managers should keep a close check on Cloud usage to ensure that costs are within budget. Another reason for them to keep close check is due to the fact that migrating data sets on the premise and processing business workloads to the Cloud is a highly complex process for companies.
Making the Big Data system accessible
Another major challenge that most organizations face is making the Big Data system accessible to analysts and data scientists, primarily in a distributed environment that has diverse data stores and platforms. To assist analysts in the organization to get the specific data, the analytical and IT teams should work non-stop to create data catalogs that implement management of metadata and data lineage tasks. The governance of data and its quality should be the database administrators’ priorities to ensure all the sets of Big Data are consistent, clean, and properly used.
Collection of Big Data regulations and practices
For several years, organizations have limited restrictions on collected data sourced from business customers. However, with the use and surge of Big Data, the cases of data misuse have increased. Customers and people in the market are concerned about the mishandling of their personal information, and some have been victims of a data breach. This has led to a demand for laws revolving around the collection of data and their transparency. An increase in the privacy of customer data has become the need of the hour for most organizations.
In the above context, there was an outcry revolving around violations that center around personal privacy. This resulted in the European Union passing the General Data Protection Regulation or the GDPR w.e.f. May 2018. This Regulation sets a limit of the data types that businesses collect and needs consent from individuals to opt-in to collect personal data and other compliance with legal grounds. It also includes a provision specifying the “right-to-be-forgotten” that allows the EU residents to ask organizations to delete their data.
Experienced database administration and management experts from the esteemed company, RemoteDBA.com state, there are no similar federal laws in the USA with respect to the above. However, in California, the CCPA or the California Consumer Privacy Act targets to provide its residents with increased control over the use and the collection of personal information issued by organizations. The CCPA was signed in 2018 legally to take effect from 1st Jan 2020. Besides the above, officials of the US government are scrutinizing practices deployed to manage data, particularly for those organizations that collect data from their customers and sell this information to other companies for unknown uses.
An insight into the human aspect of analytics for Big Data
Finally, it can be safely said that the effectiveness and value of Big Data depend upon those given the onus of understanding this data and creating the correct queries for directing analytics for Big Data projects. Big Data has some tools that match their specialized niches for enabling less savvy technical users to deploy daily business data in applications that deploy predictive analytics. Additional technologies like the Big Data appliances based on Hadoop help organizations incorporate a feasible computer infrastructure for managing projects revolving around Big Data while reducing the need for distributed software knowledge and hardware. In conclusion, experienced database administrators and managers say Big Data can be contrasted along with small data. This is another popular term deployed for describing data whose format and volume can be used easily for analytics that need self-service. A common axiom popularly quoted saying that big data is for the use of machines while small data is used for people.