Why do you need Big Data best practices? Whether it’s government, private organizations, military, healthcare, or agencies such as NASA, there is no better option to meet analytics needs, increase productivity, and become more efficient.
Big Data has become the fulcrum on which organizations of all sizes and shapes revolve for quite some time now. The integration of analytics tools and Big Data management has been the hallmark of transformational technologies organizations are adopting.
A whopping 94% of organizations believe that data and analytics solutions are critical for growth, according to research. Big Data has come to be of practical use, and you can gather veritable insights from the avalanche of data that can easily be at your disposal to monitor the performance of machines and gadgets.
The problem data scientists and data analysts may face in certain organizations is the ease of getting the best practice for using Big Data right at the onset, and that can be a bit frustrating. If you belong to this group, you don’t need to fret because you are not the only one; moreover, you can solve these problems with the following steps.
Everybody is talking about technology; after all, it’s the world of digital transformation. But looking at Big Data analytics, you may not be able to keep up with the pace at which it’s evolving.
It has now become possible for data management and analytics teams to handle large volumes of data and analytics complexity, which was only possible for large organizations and governments previously. However, you must not adopt any new technology just because everybody is doing so; you must consider how the adoption will positively or negatively impact your organization.
Considering real-time data analytics, you need to answer the following questions: Can you understand and work with the level of detail data is generated and collected? Do you have the capabilities to make insightful decisions at the speed you collect data?
Policymakers in different organizations, data scientists, and data analysts get frustrated when they realize that their actions lag behind the data analysis, which means that money has gone down the drain. The focus now must be on right-time analytics rather than real-time analytics by policymakers to avoid wasting funds unnecessarily.
Your team of data scientists and analysts may feel overwhelmed by the large volume of data you have and see big data as part of that problem; you don’t want to stress them with such data volumes, so go ahead to leverage enterprise AI tools and machine learning for veritable insights. Since you will focus on real-time analytics, you may need to gather and store the data for future use.
The insights from the data can enable your data analysts to discover patterns that can lead to the discovery of problems and opportunities that can significantly enhance decision-making in the organization. You can ensure that the volume of big data does not overwhelm you by leveraging technological advancements such as machine learning and AI.
What is your big data strategy? How do you intend to deploy the best analytics tools for effective decision-making? These are areas you need to prioritize.
One essential factor data scientists use for data discovery and analysis is data visualization. Data visualization enables data scientists to write clustering algorithms effortlessly because they have acquired the relevant coding skills.
It’s hard to work at scale with data and depend on the ordinary visual capacity to make relevant sense from large volumes of data. However, with the appropriate data visualizations, you are almost at par with a data scientist in picking out close data points in any chart and programmatically finding outliers in a big data set that may be difficult to the ordinary eyes.
Data visualization is not easy, especially in predictive analytics apps, where technical savviness is necessary for an accurate interpretation of data details; you can get over the difficulties of your decision-making process with properly designed visual representations of your data and analytical results. A well-focused big data strategy must include appropriate data visualization tools, and at the same time, it should enhance the acquisition of relevant training by analysts and data scientists.
Managing big data must be done at scale, and you must take cognizance of its diverse nature. For instance, you may wish to store audio recordings of your client support calls in a big data environment, which will go along with product images, transactions and operational records, and other documents.
Based on the diversity in the data, it may not be possible for you to think up all the use cases and requirements in advance. At the same time, it’s practically impossible to assume that you can use one project to establish all the relevant analytics scenarios.
It’s something your analytics team will develop over time based on changing organizational needs and technological advancements. You may not need to structure your data when you first process and store it; you can leave it in the unstructured format and then filter, transform, and organize it as needs for any new analytics app arise; this is the essence of future-proofing.
Future-proofing is an approach you need for an actionable long-term big data strategy.
On-premise data storage is no longer an option based on the high cost and large volumes; cloud services will remove the barriers to your big data strategy. With cloud vendors, data storage is priced as a commodity compared to buying your own on-premise devices.
Some add-ons from cloud vendors include data security, archiving, replication, availability, backup, and restore. Being professionals, cloud vendors should have better tools, highly experienced staff, and advanced processing capacity.
With privacy legislation such as the GDPR or vertical regulations such as HIPAA, you don’t have any option but to implement strong data governance. Your big data strategy must focus unwaveringly on regulatory compliance.
Regulatory compliance and data governance should not be solely for keeping the laws; they enhance better resources for big data analytics.
Despite the ease of working with well-governed data, data scientists and analysts are more innovative and confident in freely exploring and experimenting with data aggregates.
Developing a big data strategy is not a walk in the park; it requires vital inputs from data scientists, IT teams, analytics leaders, and data managers. To ensure that you are not flushing money down the drain by nose-diving into technological advancements, you must look at big data as an asset with organization-focused analytics.