How To Build An Effective Data Strategy To Transform Your Business

What is the Definition Of Modern Enterprise Data Strategy?

The goal of an enterprise data strategy is to use a range of data to support the company's overall business plan and to be able to define essential data assets, how data generates value, what is a data ecosystem, and how data governance and compliance are defined.

Zettabytes of data are being generated through social media, the Internet of Things, sensors, autonomous vehicles, and multimedia material around the world. The information may come from private, public, or third-party sources and is not arranged in any particular way. Enterprises required data to be kept and processed at edge Locations, cloud-enabling data center disaggregation, and data center-like technologies are emerging at the network's edge, all of which necessitate data capture, storage, and analysis. data storage is a key part of developing an enterprise data Strategy, and estimating the Total Cost of ownership is tricky since it's not about the cost of storing data, but rather the cost per operation of accessing and analyzing the data.

Modern Enterprise Data Strategy Capabilities

Enterprise data engineering solutions demonstrates an organization's skills in terms of leveraging data to enable reporting and decision systems that have a positive impact on business outcomes. Capabilities in Defining Modern Enterprise Data Strategy

Workflow Management and Success Factors for Analytics

Any Data Platform's ultimate purpose is to enable Analytics, which can assist an organization in analyzing its current status and making better decisions. It is critical to have a well-defined documented workflow that walks through the entire process while describing the reusable layer for data integration and analytics. This will assist any organization in swiftly constructing an analytics platform.

The processes for collecting, managing, and integrating various types of data sources into the Analytics Platform should be included in the Enterprise Data Strategy. It should also demonstrate how Agile Deployment Methodologies may quickly move Analytical Models from Experiments to Production. Before beginning any Analytic Project, a proper Acceptance Criteria should be established, which should include essential aspects such as what current problems it uncovers in the present system and how it will help an organization expand.

Aligning the Technology Team with the Business

Once the analytical workflow has been established. It's critical to take a wide view of our existing Business Implementation situation. Building a Data Strategy requires a thorough understanding of the current state of the business and its requirements. After that, all tools, frameworks, and technologies should be used. The primary goal should be to spend time determining what the business needs are for any project, and then designing the Analytics Platform Architecture accordingly.'

Identifying Key Data Sources and Managing Hybrid Data

Bringing all or as much data is a common mistake when constructing an Analytics Platform, and handling that much data comes at a high expense. So, before implementing the Data Integration Process, it's critical to identify the essential data sources required by the Analytics Team. This will assist keep Data Integration costs low and Data Analysts trust in the data.

Sources of Real-Time Data Integrating with batch data sources can assist firms in making judgments about business processes that require quick action. Hybrid Data Management specifies how real-time and batch data will be managed and supplied to the analytics team in order to provide business teams with near-real-time insights.

Choosing the Best Data Management, Analytics, and Visualization Tools and Processes

The frameworks, technologies, and analytical and visualization tools that are used are entirely dependent on the use cases. The Business and Analytics Team can communicate a variety of factors with the data engineering services about how they will access the data. These are the three use cases that the Analytics Platform requires.

• Reporting Dashboards: These are dashboards that are designed for the business team and provide business insights from the analytics team. They typically involve statistical analysis.

• Advanced Analytics / Decision Systems: These Dashboards provide insights that statistical analysis cannot provide. To evaluate the data and create business insights, Machine Learning and Deep Learning approaches are used.

• Ad Hoc / Data Discovery Systems: For their various requirements, Data Engineering, Analysts, and Business Teams typically use this form of Access Pattern.

The Data Engineering Team uses this to explore the data they acquire from various data sources and model the Data Warehouse accordingly. Data that has been transformed or processed is kept in the warehouse. However, Data Scientists are occasionally curious about the current condition of data. They employ data exploration tools to investigate the data in Data Lake. These are the four characteristics that aid the Data Platform Team in designing storage and querying engines for the Analytics and Business Teams.

Query Pattern

The data engineering solutions meets with the Data Science and Business Intelligence teams to discuss how they will query the data and, as a result, a Data Storage Strategy is created. The Query Pattern can be predefined in both Reporting Dashboards and Decision Systems, but not for Ad Hoc Queries.

Concurrency

Query per second (QPS) is a critical need for the Reporting Dashboard and Business Team in general. As a result, Data Strategy must be structured in such a way that data may be served to various consumers at the same time.

Read Latency

specially reporting dashboards, like concurrency, require immediate returns rather than queries that take minutes or hours to complete. For serving the results to the business Team fast, OLAP cubes, fast data stores like elasticsearch, or key-value stores are used. The system should also provide low-latency data to decision systems, allowing decision systems to operate in near-real-time. lower read latency is not predicted for Ad hoc query requirements.

The quantity of data

The concept of hot and cold storage is employed to reduce storage costs as low as possible. Data Retention Periods are often set, allowing the Data Engineering Team to design the process of archiving data to low-cost storage such as S3 Buckets, AWS Glaciers, and so on.

For most use cases, such as the last 3 months or 6 months data, reporting dashboards and decision systems simply require current data. However, determining this for Ad Hoc queries is difficult. It is therefore OK if Ad Hoc queries take a lengthy time to provide results.

Data Governance Enables a Secure Data Sharing Process

Engineering, Data Analysts, Data Science, BI Team, and Application Development Team are all involved in creating a successful analytics platform. An Enterprise Data Strategy should specify how data will be securely shared among diverse teams. There are times when data should be shared with the Data Science Team for the purpose of training their decision systems for a limited duration. It should be able to provide Limited Time Data Access while still concealing sensitive data from data analysts.

It's critical to lead all teams in developing this Secure Data Sharing Culture. They should not feel constrained in their access to the Data. Instead, they should be informed on why Data Governance is necessary and how it benefits both individuals and the organization.

Providing business-relevant analytical capabilities and tools

There should be a clear process in place that explains how to provision resources to different teams. The Technology Team should have a strong grasp of Resource (Memory, RAM) Allocation to Data Engineering, Data Exploration, Data Analyst, and Data Science Activities because there are a lot of activities going on by many teams in a company.

Additionally, Provisioning of Business Relevant Tools such as BI Tools and Other Reporting or Analyzing Tools should have a defined procedure for "Who can access which level of BI Tools and also Enabling RBAC on Reporting Dashboards," which will help us protect Confidential Reports from the BI Team and make them accessible only to those who need to know.

Final Thoughts on Enterprise Big Data Strategy

Technologies, Frameworks, and Tools will continue to emerge, and the Technology Team may be eager to switch from one framework to another swiftly and correctly. However, the Technology Team must consider constructing their architectures in an evolutionary manner so that any changes in tools and frameworks do not disrupt the overall equilibrium and can be integrated effortlessly.

The data engineering services should be kept up to date on new data storage capabilities and requirements, as well as their data and analytical workflow management.