Why Modern Enterprise Data Strategy
Enterprises must create a Comprehensive Data Strategy to leverage a variety of Data to support the Company’s overall business strategy and able to define critical data assets, how does data generate value and what is data ecosystem, how do we define data governance and compliance
The world is witnessing Zettabytes of Data from social media, the Internet of Things, sensors, Autonomous Cars, and multimedia content. The data may be from private, public or third-party sources and include information not organized in a predefined manner.
Enterprises required data to be stored and processed at EDGE Locations, Cloud enabling disaggregating of data center and data center-like technologies are appearing at the edge of the network, all requiring data capture, storage, and analysis
Data Storage is an important aspect in defining enterprise Data Strategy and calculating the Cost of Ownership is difficult as it’s not about and securing data cost ( Whether it’s Cold or Cool Storage ), its about the cost per operation of accessing and analyzing the Data.
An Enterprise ability to derive value from its data defines its maturity. There is a natural progression from the data strategies of today to the cognitive-enabled data strategies of tomorrow. An analytically mature organization will find it valuable to add cognitive technologies to its data strategy which directly impacts business success.
Key Capabilities of Modern Enterprise Data Strategy
Enterprise Data Strategy shows the capabilities of an organization that how they can leverage their Data to enable their Reporting and Decision Systems efficiently which impacts the business Outcome
Capabilities in Defining Modern Enterprise Data Strategy
1). Analytics Workflow Management and Success Criteria
The End goal of any Data Platform is to enable Analytics which can help an organization to analyze their current state and help them to take better decisions.
It is important to have well defined documented workflow which should walk through the complete process explaining Reusable layer for Data Integration and Analytics Process which will help any Organization to build Analytics Platform quickly. It should include the Processes that how different types of Data Sources can be collected, managed and integrated into Analytics Platform. It should also show the capabilities of taking the Analytical Models from Experiments to Production quickly using Agile Deployment Methodologies.
A Proper Acceptance Criteria before starting any Analytic Project which should include the key points that what current problems it finds in the existing system and how it will enable business growth for an organization.
Analytical Projects often takes weeks or months or years to complete. So, It is important to define success criteria in the short term and long term. Ideally, Analytical Teams pick one business objective and execute it end to end for shorter term success plans and Alongside, keep working on their Long Term goals as well.
2). Aligning Technology Team with Business Perspectives
Once Analytical Workflows are in place, It is important to have a broader view of the current state of our Business Implementation. Good Understanding of Business State and Requirements is the key to building a Data Strategy. All Tools, Framework & Technologies comes afterward, The Very first Goal should be to spend time on what are the business requirements for any Project and Then accordingly design the Analytics Platform Architecture.
3). Defining Key Data Sources and Hybrid Data Management
Bringing All or As much Data is a common mistake while building Analytics Platform and it often puts a big cost on managing that much of Data. So, Before Implementing Data Integration Process, It is very important to find the relevant data sources required by Analytics Team and it will help to keep cost reasonable for Data Integration Activities and keep Data Analysts confident about the Data.
Real-time Data Sources Integrating along with Batch Data Sources can help organizations to take decisions on those Business Process which needs immediate action. Hybrid Data Management allows how Real-time and Batch Data will be managed and served to Analytics Team to enable near real-time Insights for Business Team.
4). Choosing the Right Set of Tools & Processes for Data Management, Analytics, and Visualization
Selection of Frameworks, Technologies and Analytical & Visualization Tools entirely depends on specific use cases. There are a lot of Factors which Business and Analytics Team can share with Data Engineering Team regarding how they will access the Data. These are the 3 use cases which are required for the Analytics Platform.
- Reporting Dashboards: Dashboards Designed for Business Team which provides Business Insights from the Analytics Team and these types of dashboard generally use Statistical Analysis ( Standard SQL Queries ).
- Advanced Analytics / Decision Systems: These Dashboards provides insights which are beyond the capabilities of Statistical Analysis. Machine Learning and Deep Learning Approaches are applied to analyze the Data and derive Business Insights from it.
- Ad Hoc / Data Discovery Systems: This type of Access Pattern is generally used by Data Engineering, Analyst and Business Team for their different requirements.
Data Engineering Team uses this to explore the Data they are collecting from different Data Sources and accordingly Modelling the Data Warehouse.
Transformed / Processed Data is stored in Warehouse. But sometimes, Data Scientists are interested to see the actual state of Data. Data Exploration tools are used by them to explore the data in Data Lake.
And These are the 4 factors which help the Data Platform Team to design their Storage and Provision Querying Engines for Analytics and Business Team.
Query Pattern: Data Engineering Team generally discusses with Data Science and Business Intelligence Team that How they will be querying the Data and Accordingly Data Storage Strategy is designed. In both, Reporting Dashboards and Decision Systems, Query Pattern can be predetermined, but it is not possible for Ad Hoc Queries.
Concurrency: Generally, QPS ( Query per second ) is a major requirement for the Reporting Dashboard and Business Team. So, Data Strategy must be designed so that Data can be served to multiple users concurrently.
Read Latency: Along with Concurrency, Specially Reporting Dashboards expects results in quick time instead of queries running for minutes or hours. OLAP Cubes, Fast Data Stores like ElasticSearch or Key-Value Stores are used for serving the results to Business Team quickly. The system should also serve data to Decision Systems with low latency to enable Decision Systems in near real-time. For Ad hoc Query Requirements, Lower Read Latency is not expected.
The volume of Data: Concept of Hot Storage and Cold Storage are used to keep Cost optimal for the Storage. Generally, Data Retention Periods are defined which allows Data Engineering Team to define the process of archiving data to some Lower Cost Storages like S3 Buckets, AWS Glaciers, etc.
Reporting Dashboards and Decision Systems generally needs only recent data for most of the use cases like Last 3 months or 6 months Data.
However, It is hard to determine this for Ad Hoc queries. So, It is fine if Ad Hoc queries take a long time to return the results.
5). Unified Data Catalog for Data Discovery and Exposing Syndicated Data via APIs
A Lot of Data Stores have different capabilities and they are used according to use case requirements. So, It becomes necessary to have a Unified MetaData System which can provide a view of All Data Stores at a single place. This will enable Data Consumers like Analytics Team, Engineering Team to easily the Data Knowledge like Schemas, Fields, etc.
Enabling Machine Learning on Data Catalog for Data Tagging allows Data Analyst, Scientists to better understand the Data. They can easily know that What this Data means and What type of information it is providing.
Along with this, Organizations do need Data Exposing Capabilities to third parties often called Data as a Service. Data Catalog eases this process by visualizing all MetaData at a single place and with the help of Data Governance and Business Team, Required Syndicate Data can be exposed easily using APIs.
6). Enabling Secure Data Sharing Process with Data Governance
Various Teams are involved in making a Successful Analytics Platform like Engineering, Data Analysts, Data Science, BI Team and also Application Development Team.
An Enterprise Data Strategy should elaborate on how Data will be shared across various teams in a secure manner.
There are use cases when Data should be shared for a limited time with the Data Science Team for Training of their Decision Systems. It should be capable of enabling this Limited Time Data Access and also Hiding Sensitive Information from Data Analysts.
It is very important to guide all teams to build this Secure Data Sharing Culture. They should not feel that they are restricted to access the Data. Instead, They should be educated regarding why Data Governance is required and how it is beneficial for them as well as for an organization.
7). Provisioning Business Relevant Analytical Tools and Capabilities
A Clear Cut Process should be there which should explain the procedure of provisioning the resources to various teams. There are a lot of efforts going on by various teams in an organization and Technology Team should have a good understanding of Resource (Memory, RAM ) Allocation to Data Engineering, Data Exploration, Data Analyst and Data Science Activities.
Along with this, Provisioning of Business Relevant Tools like BI Tools and Other Reporting or Analyzing tools should have defined procedure that Who can access which level of BI Tools and Also Enabling RBAC on Reporting Dashboards will help us to protect the Confidential Reports from BI Team and accessible only to concerned persons in the organization.
8). Master Data Management for Customer-Centric Analytics
Each Organization wants to serve his/her Customer in the best possible way and want to provide personalized services. Data is integrated from various Data Sources into the Analytics Platform and System should be capable of identifying Customers Data coming from disparate Data Sources.
Machine Learning Approaches are applied to mine the Data and identify which data is linked to which customer and according to training the Decision, Prediction Systems and Recommendation Engines.
Master Data Management enables us to maintain All Customer and Other Referential Data and Also maintains a record of how it maps to data coming from various sources. Personalized User Experience and Customer 360 Degree View should be the end goal of Master Data Management.
9). Data Quality Management for Improved Decision Making
Data is of no use if Data Consumers don’t know how accurate the Data is. Decisions made on wrong data can lead to massive losses in business and Company can lose their Customer as well due to wrong or irrelevant recommendations. Just Imagine If Customer Gender Identifier Service classifies the wrong gender of any Customer and If There is some Gender-based Recommendation Service, Then Organization might lose that customer in case of the wrong recommendation.
So, Data Quality becomes the most important requirement before Any Data Analyst or Scientist make use of it. Enterprise Data Strategy should be able to give clear KPIs which will determine the quality of Data and What action needs to be taken if Data Quality Requirements not Met.
Data Quality should be applicable on various Data and Analytics Processes like ETL Pipeline, Data Warehouse Testing, Batch Data Migration Testing, Daily Reports Testing and a lot more.
10). End to End Lineage for Data Reliability, Efficiency, and Security
End to End Lineage means capturing all events happening across the Analytics Platform. Data Lineage Service should be capable of capturing events from ETL Jobs, Querying Engines, Data Processing Jobs and There should be Data Lineage as a Service as well which can be integrated by any other services in which there is need of incorporating Data Lineage.
End Goal of Data Lineage should be to provide a detailed view of which user is accessing data in which service. Along with that, Capturing Querying Engine Lineage can allow Engineering Team to see the Querying Patterns which Analyst or Science Team is using and Accordingly Engineering Team can optimize the storage. Another use case is to optimize the cluster resources by capturing the Infrastructure Lineage like capturing Logs of Data Ingestion & Data Processing Logs.
11). Cost Effectiveness and Performance using Modern Data Engineering Capabilities
Enterprise Data Strategy should always consider Cost Optimization while choosing any framework, technology or even during define a workflow.
It depends on two things:
- Data Storage & Access Strategy
- Choosing Right Kind of Architectural Patterns
Data Storage & Access Strategy
- Choosing Right Storage Format & Compression Algorithms like Columnar Format with Snappy, BZip Compression to reduce Disk IO and low latency results for Analytical Queries.
- Identifying that Which Data & How much Data needs Hot Storage and Rest can be archived to Cold Storage.
- Properly Partitioning of Data will also help to reduce the Disk IO and guiding the Analyst and Data Science Team to use Partitioning Strategy can play a vital role in cutting the Data Accessibility Cost.
Choosing Right Kind of Architectural Patterns
- Serverless Architectures are becoming very popular and All Technology Teams should always be designed their architecture around this pattern. The Key benefit of Serverless Architectures is to enable “Pay for What’s being Used” pattern.
- Serverless Capabilities can be introduced in Micro Services, ETL Jobs, Querying Engines and even in Serving Advance Analytical Models as well.
12). Technology and Vendor Agnostic Data Strategy
There is a lot of competition going on between various open source and cloud providers for various services they provided either for storage, processing or microservices, etc.
There is every chance that the Organization may want to switch to some other cloud or open source to optimize their cost or performance.
So, Always Prefer choosing those tools, services or frameworks which can be easily integrated with Other Cloud Services or can be migrated easily. Vendor Agnostic Approach should be used while selecting any kind of frameworks, Otherwise, Organization will stick in Vendor Locking Technologies and Extra Efforts will be required for migrating from one service/tool/framework to another.
Concluding Enterprise Big Data Strategy
Technologies, Frameworks or Tools will keep on coming and Technology Team might feel excited to shift from one framework to another quickly and of course, the right thing to do. But Technology Team must think of building their architectures in an evolutionary way so that New Change should not disturb the whole equilibrium and New Changes either in Tools, Frameworks can be introduced seamlessly.
Data Strategy Team should always be updated with coming Data Storage Capabilities and Requirements and keep their Data & Analytical Workflow Management updated.
A Holistic Strategy
Effecting the dynamic, ever-growing landscape of data technology and undulating businesses need clear and consistent communication and path.
To stimulate connected improvement in your data strategy as you evolve it for each technology and business initiative, we recommend taking the following steps:
- Read the “Big Data Engineering” Use Cases.
- Take a Data Assessment Strategy to determine your Analytics IQ
- Learn more about XenonStack “Big Data Strategy and Consulting Services“ or Contact us about data strategy and advisory services including review of your current efforts
Businesses that adopt holistic data strategy are able to manage the hurdles of adopting innovation efficiently into existing movements. Without holistic data strategy, organizations risk internal miscommunication and inefficient use of data technology, delay time to poor market solutions. Quicken your digital transformation and drive data strategy now.
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