Tech

Business Transformation – Enterprise data management strategies

Companies had better put up an efficient data strategy in order to take advantage of different data to assist their business operation and be able to define important data assets, how data creates value and what data ecosystem truly means as well as how you define data governance and compliance.

  1. Data Management – why modern enterprise data strategy is important

Companies had better put up an efficient data strategy in order to take advantage of different data to assist their business operation and be able to define important data assets, how data creates value and what data ecosystem truly means as well as how you define data governance and compliance.

The current society is realizing that there are even zettabytes of data from social media, the Internet of Things, sensors and media sites. The data would be generated from a private source or public or third party one and come with information which is not arranged in a predefined way.

Companies needed data that should be stored and processed at EDGE Locations are turning up more and more, all of whom are in need of data capture, storage and analysis.

Data Storage is a necessary element in defining company Data Strategy and measuring the cost of ownership turns out to be a difficult task because it is not just about securing data cost. Actually, it is about the cost per operation of approaching and analyzing the data.

The company ability to capture value from its data means its maturity. There is a progression of the data strategies nowadays to data strategies of the upcoming time. A mature company will find it ideal to apply cognitive technologies to its data strategy which will directly have an impact on business success.

  1. The major capabilities of modern enterprise data strategy

Enterprise data strategy will show the abilities of a company that how they could take advantage of their data to allow for their efficient reporting and decision systems which will have an influence on the business outcome. Below are some of the abilities in defining modern enterprise data solution.

The first capability is managing the analytics workflow and success criteria. The major goal of any data platform is allowing for analytics which could support a company to analyze their existing state and help them make more informed decisions.

It is critical to have a well-defined workflow which should go through the entire process in explaining reusable layer for data integration and analytics process that will help any company to put up analytics platform at a fast pace. It should come with the processes that how various kinds of data sources could be gathered, managed and integrated into analytics platform. IT also had better point out the abilities of taking the analytical models from experiments to production fast with using Agile Deployment Methodologies.

Suitable acceptance criteria are needed before you start any analytic project including the major points about what existing issues it finds in the current system and how it would boost business development for a company.

Analytical projects often require weeks or months or even years to finish. Therefore, it is vital to define success criteria in both short term and long term. And Analytical Teams choose one business goal and exploit it for shorter-term success plans and continue working on their long-term goals also.

Secondly, technology team will also be aligned with business perspectives. When Analytical Workflows are in its right place, it is essential to have a wider view of the existing state of your business adoption. A good understanding for your business state and demands is the main point to putting up a data strategy as well as all tools or technologies later. The most prominent goal would be taking time to do what the business requirements for any project need and then designing the Analytics Platform Architecture in a proper manner.

Key data sources and hybrid data management solutions should be defined as well. Bringing all or much data is a common error when organizations are putting up analytics platform. This would lead to a big cost on dealing with that huge amount of data. Therefore, before you adopt data integration process, it is so important to look for the only relevant data sources needed by the analytics team and this could help reduce the cost needed for data integration activities and keep data analysts confident about the data quality.

In addition, real-time data sources which are integrated along with batch data ones could help companies to make decisions on those business process that requires instant actions. Hybrid data management also enables how real-time and batch data could be managed and delivered to analytics team to allow for insights in near real time for business team.

The next ability is opting for the right set of tools and processes for data management, data analytics and data visualization. Choosing frameworks, technologies as well as other tools for analytical and visualization will totally rely on particular use cases. There are a lot of different elements which business and analytics team could share with data engineering team in terms of how they could approach the data. there are three major use cases that are important to an analytics platform.

Reporting dashboards are the ones specifically made for business team which offers business insights from the analytics team and these kinds of dashboard often make use of statistical analysis. On the flip side, advanced analytics or decision systems are the dashboards providing insights which are more than just the abilities of statistical analysis. Machine learning and deep learning approaches are used to make analysis for the data and capture business insights from it. Last but not least, ad hoc or data discovery systems are the types of access pattern, which is utilized by data engineering, analyst and business team for various demands.

Data Engineering Team will take advantage of this in order to discover the data they are gathering from various data sources and modelling the data warehouse in a proper manner. Transformed data or processed one is kept in data warehouse. Nevertheless, in some cases, data scientists may prefer to view the current state of data. In this situation, they will make use of data exploration tools in order to see the data in data lake.

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