Enterprise Data Management: Implementation Plan, Talents and Tools
ScienceSoft provides data management services to help companies ensure high data quality, safe and easy data access for informed decision-making across the company.
Enterprise Data Management: The Essence
Enterprise data management involves establishing data governance (standards and policies to organize and manage data access, collection, storage, analysis, security, etc.) and completing individual data management projects to implement data governance across the company.
According to ScienceSoft's experience with real-life projects, enterprise data management can be briefly described as follows:
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Enterprise data management implementation timeframes: Full-scale data management implementation for a 5,000-employee company may take from 24 to 36 months. |
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Key steps: Enterprise data management strategic planning; planning, development and launch of constituent data management projects. |
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Enterprise data management team: Project manager, system analyst, solution architect, data engineer, DevOps engineer, QA engineer. |
Enterprise Data Management Implementation Plan
The suggested plan is based on ScienceSoft’s 33-year experience in providing data analytics and data management services. It features a sample procedure we follow when establishing enterprise data management for our customers.
Enterprise data management strategic planning
Step 1. Defining data management objectives
Data management objectives are based on the overall corporate objectives and used when establishing data governance policies and KPIs to assess data management implementation success. Common enterprise data management objectives usually include:
- Data is easily accessed, used properly and to its max value.
- Data is of acceptable quality.
- Data is secure.
- Data is compliant with standards and regulations (GDPR, HIPAA, etc.).
ScienceSoft’s best practice: When defining data management objectives, our consultants interview all stakeholders to collect their needs, goals, and vision of the successful data management project implementation. It helps understand their priorities, plan the development process accordingly and as a result - provide a satisfactory end product.
Step 2. Evaluating the current state of data management
It’s necessary to analyze:
- Existing data architecture and data flows.
- Data source systems (their number, connectivity between systems, who has access to them, etc.) and data stored there (data type and structure, volume, sensitivity level, etc.).
- Applied data security practices (data access and usage policies, who monitors compliance with data security policies, etc.).
- Data quality management practices (established data quality metrics, data auditing policies, etc.).
- Master and metadata management practices, etc.
Step 3. Establishing data governance
- Defining data governance policies and standards for each enterprise data management component:
Data architecture
System architecture diagrams, entity-relationship diagrams, data flow diagrams, etc., which provide an overview of how data is captured, ingested, transformed, modeled, and put to usage across the company.
Data integration
Data integration policies and standards for consolidating data from disparate sources into a single dataset via ETL/ELT processes or data virtualization.
Data quality management
Rules for data quality (e.g., data profiling, cleansing, and matching) to ensure data is fit to serve its objectives.
Data storage
Policies for controlling the data storage environment (stored data types, data location, recovery procedures, storage environment performance, etc.).
Data security
User authentication and authorization policies, data access policy, data audit policy, etc., to prevent unauthorized data access and its inappropriate usage.
Reference and master data management
Data deduplication and standardization policies to enable master and reference data consistency across transactional and business intelligence systems.
Metadata management
Business glossaries and data lineage traceability documents, which maintain information on data definition, relationships, movement, etc.
Data warehousing, analytics, and reporting
Policies for providing and overseeing data analytics processes and infrastructure.
Content management
Policies for storing, sharing, indexing and auditing corporate data stored outside relational databases for maintaining data integrity and enabling easy data access.
Implementation of data management projects
Once data governance is established, an enterprise can define an enterprise data management implementation plan and create implementation roadmaps for the entire program and then for each data management component.
ScienceSoft implements a data management program with the following steps for its elements:
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Requirement engineering and architecture design
- Engineering requirements for a data management element.
- Reviewing technology stack currently used to enable the element.
- Outlining the optimal architecture and feature set for the future technical solution.
- Defining the optimal tech stack.
2
Planning the implementation of a data management project
- Determining a data management project scope, deliverables and timeframes.
- Defining a data management project team and designing collaboration workflows.
- Developing a project KPI suite based on the set data governance standards.
- Outlining possible project risks and defining a risk mitigation strategy and plan.
- Deciding on the relevant sourcing model.
- Estimating efforts, TCO and ROI for the data management project.
ScienceSoft’s tip: Our practice has shown that effective data management project planning can help reduce project time and budget by up to 30%.
3
Technical solution development and launch
- Implementing a technical solution for each data management component and integrating it into the existing IT environment.
- Deploying the tech solution in production.
- User acceptance testing.
- User training.
ScienceSoft’s best practice: To deliver a tech solution for each data management component in the shortest time possible, in our projects we opt for DevOps-driven iterative development – it assures the quickness and frequency of releases without sacrificing the solution’s quality.
Consider Professional Services for Enterprise Data Management Implementation
Enterprise data management consulting
- Defining data management objectives.
- Evaluating the existing data management maturity level.
- Creating a business case for a data management program.
- Establishing data governance.
- Planning out data management implementation.
- Defining the architecture and optimal tech stack.
Enterprise data management implementation
- Defining data management objectives.
- Evaluating the existing data management maturity level.
- Establishing data governance.
- Architecture design and software selection for a technical solution(s).
- Development and stabilization of a tech solution for each data management program element.
- Tech solution launch.
ScienceSoft as a Trusted Data Management and Data Analytics Partner:
When we first contacted ScienceSoft, we needed expert advice on the creation of the centralized analytical solution to achieve company-wide transparent analytics and reporting.
The system created by ScienceSoft automates data integration from different sources, invoice generation, and provides visibility into the invoicing process. We have already engaged ScienceSoft in supporting the solution and would definitely consider ScienceSoft as an IT vendor in the future.
Heather Owen Nigl, Chief Financial Officer, Alta Resources
Why ScienceSoft for Enterprise Data Management?
- Data analytics and data science services since 1989.
- Data warehousing and BI expertise since 2005.
- Big data services since 2013.
- Dedicated data management team of data management consultants, solution architects, data engineers, etc.
- Expertise in 30+ industries, including manufacturing, retail and wholesale, professional services, healthcare, financial services, transportation and logistics, telecommunications, energy, and others.
- ISO 9001 and ISO 27001 certified to assure proper quality management procedures and the security of the customers’ data.
- For the second straight year, ScienceSoft USA Corporation is listed among The Americas’ Fastest-Growing Companies by the Financial Times.
Our Data Management Portfolio
Customer Data Management and Analytics Solution
- Big data management platform for data aggregation from 10+ sources.
- 30-dimension ROLAP cubes for regular and ad hoc reporting to enable user engagement assessment, user behavior trend identification and user behavior forecasting, etc.
Big Data Management and Analytics Solution for IoT Per Trackers
- Big data solution for processing 30,000+ events per second from 1 million devices.
- Real-time location tracking.
- Push notifications on critical events.
- Hourly, weekly or monthly reports on a pet’s presence.
Data Management and Analytics Solution for the Automotive Industry
- ETL-based BI solution with a staging area, DWH database and data marts.
- Multidimensional analytical cubes.
- 40+ customizable reports and dashboards to track KPIs, assign tasks and goals, and share important information.
Airline Market Data Management and Analysis
- Data warehouse deployed in ScienceSoft’s data center.
- 10-dimension OLAP cube to analyze the 10-year history of external data.
- Web-based reporting with self-service capabilities.
Advertising Channel Data Management and Analytics Solution
- Big data warehousing solution for processing 1,000+ raw data types.
- 5-module analytics system to analyze advertising channels in 10+ countries.
- Up to 100 times faster analytical query processing.
Data Management Solution for Customer and Retail Analysis
- Data hub and data warehouse to ingest and store data from 15 data sources.
- An analytical server with 5 OLAP cubes and about 60 dimensions overall.
- 90+ business reports.
Program manager
- Outlines the scope of the data management program.
- Determines the efforts, timelines, KPIs, and deliverables for the data management program and program elements.
- Assembles and helps establish the collaboration within the team involved in the enterprise data management program.
- Monitors and manages the costs of the program and program elements.
- Oversees the overall program execution.
Data management consultant
- Communicates with stakeholders to elicit data management needs and expectations.
- Analyzes the existing data management practices and environment (data architecture, data flows, etc.), data governance policies, etc.
- Helps conceptualize a data management program and develop an implementation strategy.
- Helps develop data governance policies and monitor compliance with them.
- Coordinates the creation of the overall enterprise data management project documentation.
- Conducts training for users (for example, on data governance policies and standards, on using new software, etc.).
Solution architect
- Assesses the existing data management environment, defines issues and optimal ways for improvement.
- Creates technical solution architecture based on the elicited business requirements.
- Recommends and implements appropriate tech approaches for enterprise data management, recommends a tech stack and assesses the business impact certain tech choices have.
- Provides supervision to development teams.
Data engineer
- Analyzes source systems, identifies data quality issues, their root causes, and steps to resolve them.
- Builds data flows, develops conceptual, physical and logical data models. Maintains data models along with corresponding metadata.
- Builds ETL/ELT processes to route source data to the destination.
- Manages master data, including creation, updates, and deletion.
DevOps engineer
- Introduces continuous integration/continuous delivery (CI/CD) pipelines to automate and streamline the development of technical solutions for data management components.
- Monitors the IT infrastructure state, handles ongoing maintenance.
QA engineer
- Designs a test strategy, create tests to evaluate the developed technical solutions for a data management program.
- Plans and coordinates testing activities, documents test results.
- Provides recommendations on data management software improvements.
Benefits of Implementing Enterprise Data Management with ScienceSoft
Guaranteed data quality
We take special care of data quality procedures to ensure consistency, accuracy, completeness, auditability, timeliness and uniqueness of your data.
BI + Big data
Our team is equally capable of deriving value from both traditional and big data.
Services from A to Z
We deliver a whole pool of services to provide our customers with comprehensive BI and big data solutions: from a solution's design to implementation.
Data Management Tools and Technologies
In our data management projects, ScienceSoft usually leverages these trusted tools:
Data integration
We use Kafka for handling big data streams. In our IoT pet tracking solution, Kafka processes 30,000+ events per second from 1 million devices.
With ScienceSoft’s managed IT support for Apache NiFi, an American biotechnology corporation got 10x faster big data processing, and its software stability increased from 50% to 99%.
Cloud data storage
We leverage Azure Cosmos DB to implement a multi-model, globally distributed, elastic NoSQL database on the cloud. Our team used Cosmos DB in a connected car solution for one of the world’s technology leaders.
We use Amazon DynamoDB as a NoSQL database service for solutions that require low latency, high scalability and always available data.
ScienceSoft used MongoDB-based warehouse for an IoT solution that processed 30K+ events/per second from 1M devices. We’ve also delivered MongoDB-based operations management software for a pharma manufacturer.
Data warehouse technologies
Our Microsoft SQL Server-based projects include a BI solution for 200 healthcare centers, the world’s largest PLM software, and an automated underwriting system for the global commercial insurance carrier.
We use Amazon Redshift to build cost-effective data warehouses that easily handle complex queries and large amounts of data.
ScienceSoft's team has implemented Oracle for software products used by GSK and AstraZeneca. We’ve also delivered Oracle-based SCM platform for Auchan, a retail chain with 1,700 stores.
ScienceSoft has used PostgreSQL in an IoT fleet management solution that supports 2,000+ customers with 26,500+ IoT devices. We’ve also helped a fintech startup promptly launch a top-flight BNPL product based on PostgreSQL.
Big data
By request of a leading market research company, we have built a Hadoop-based big data solution for monitoring and analyzing advertising channels in 10+ countries.
A large US-based jewelry manufacturer and retailer relies on ETL pipelines built by ScienceSoft’s Spark developers.
Our Apache Cassandra consultants helped a leading Internet of Vehicles company enhance their big data solution that analyzes IoT data from 600,000 vehicles.
We use Kafka for handling big data streams. In our IoT pet tracking solution, Kafka processes 30,000+ events per second from 1 million devices.
ScienceSoft has helped one of the top market research companies migrate its big data solution for advertising channel analysis to Apache Hive. Together with other improvements, this led tо 100x faster data processing.
We leverage Apache ZooKeeper to coordinate services in large-scale distributed systems and avoid server crashes, performance and partitioning issues.
We use HBase if your database should scale to billions of rows and millions of columns while maintaining constant write and read performance.
We leverage Azure Cosmos DB to implement a multi-model, globally distributed, elastic NoSQL database on the cloud. Our team used Cosmos DB in a connected car solution for one of the world’s technology leaders.
We use Amazon Redshift to build cost-effective data warehouses that easily handle complex queries and large amounts of data.
We use Amazon DynamoDB as a NoSQL database service for solutions that require low latency, high scalability and always available data.
ScienceSoft used MongoDB-based warehouse for an IoT solution that processed 30K+ events/per second from 1M devices. We’ve also delivered MongoDB-based operations management software for a pharma manufacturer.
We use Google Cloud Datastore to set up a highly scalable and cost-effective solution for storing and managing NoSQL data structures. This database can be easily integrated with other Google Cloud services (BigQuery, Kubernetes, and many more).
Data visualization
Practice
7 years
ScienceSoft sets up Power BI to process data from any source and report on data findings in a user-friendly format.
Programming languages
Practice
10 years
Projects
50+
Workforce
30
ScienceSoft's Python developers and data scientists excel at building general-purpose Python apps, big data and IoT platforms, AI and ML-based apps, and BI solutions.
Practice
25 years
Projects
110+
Workforce
40+
ScienceSoft's Java developers build secure, resilient and efficient cloud-native and cloud-only software of any complexity and successfully modernize legacy software solutions.
Practice
34 years
Workforce
40
ScienceSoft's C++ developers created the desktop version of Viber and an award-winning imaging application for a global leader in image processing.
Cloud services
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About ScienceSoft
ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. We help enterprises implement complex data management initiatives to maximize data value and enhance decision-making. Being ISO 9001 and ISO 27001 certified, we provide data management services relying on a mature quality management system and guarantee cooperation with us does not pose any risks to our customers’ data security.