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A step by step guide to everything Enterprise Data Management.

Enterprise Data Management
Enterprise Data Management
Enterprise Data Management

We live in a world where the average person creates around 1.7MB of data per second. The same is true for the various organisations around us. Modern organisations, regardless of their size, make a significant amount of data on a daily basis. 

Studies show that in 2022 alone, companies created more than 97 zettabytes of data globally and this figure will increase by leaps and bounds. The market size of enterprise data management worldwide stood at $79.7 billion in 2021 and will reach $224.5 billion by 2031, exhibiting a remarkable CAGR of 10.8% from 2022 to 2031. 

The market size alone shows how organisations worldwide perceive enterprise data management as a means to drive growth and efficiency. In the local market, the need for compliance has significantly increased in the past few years and Data is very much at the centre of these new requirements. From SMBs to large corporations, this mandates the need to have a robust plan for enterprise data management. But what exactly is it, and how can companies use a data management service to make the most of their data? Read on to discover our perspective on EDM and how it can become a force multiplier in your organisational growth story. 

The value of data exploration

One of the biggest advantages of setting up an efficient data management service is the eventual ability of an organisation to use data visualisation to get real business insights and intelligence. The service needs to help a team of analysts develop possible theories for any business-related question and then test them against the available data. Business intelligence tools and statistical programming will help achieve this, along with the tools and features available in the EDM service.  

Enterprise data management. The use case

The proper use of enterprise data management results in more efficient data transfer from various partners, applications, processes etc. The service is a central dashboard for the company to look at data and take informed business decisions. EDM helps streamline processes, and organisations plan for their future using the power of data. 

Ineffective EDM on the other hand results in lower data quality, creates conflicting data, and increases time wastage; which down the line causes more friction among team members. For EDM to succeed in internal operations, it is necessary to have a solid plan for governing, integrating, accessing and securing the company’s data. A robust data management service can help organisations organise and manage their dynamic assets and create an environment of better collaboration, decision making and communication.  

Enterprise data management. The basics

For organisations, setting up the foundation for data management starts with establishing a data governance policy. It acts as the basic framework for organising and managing data. The same approach is essential for collecting, storing, analysing, and securing data. Creating a data management service in a real-world project is divided into various sub-projects that come together to work for efficient data management.   

Over the years, we have understood that enterprise data management projects take anywhere from 24 to 36 months to complete and require the specialised skills of an experienced team that comprises a Project Manager, Solutions Architects, Data Engineers, System Analysts, DevOps Engineers and QA Engineers. The following is a broad roadmap to come up with a strategic plan that you can use for implementing an enterprise data management solution in your company:

Step 1 – Start the conversation

As in any successful project, data management to requires stakeholders to work together and communicate effectively to bring out the best results. For us as a company, the process starts with having detailed and in-depth conversations with everyone involved from the client’s side to understand every stakeholder’s needs fully. Each department in an organisation will have its own set of requirements and priorities, and these factors influence the creation of the data governance policies for the enterprise data management solution. Companies must communicate the new guidelines, standards and procedures to the last employee. Communication ensures the success of the eventual implementation. 

 

Step 2 – Identify the goal

The data management service has to align with the objectives of the organisation. Data governance policies result from carefully looking at the objectives and formulating a plan that works for everyone in the organisation. Some of the most common objectives are ensuring easy access to maintain data quality, security, and compliance with established regulations. Additional objectives can also come in depending on the nature of the business. 

Step 3 – Look inwards

Looking inwards for some could mean weighty introspection, but in this context, it means conducting an exhaustive analysis of the existing data architecture and data flows. Thoroughly evaluate data source systems, their connectivity, access levels and their stored data. The team should also examine the current data security protocols that manage data access and use policies. 

Moving forward, the team should also look at the data quality metrics along with the master and metadata management practices that exist in the data management service. This will give internal and external stakeholders a clear idea of the company’s data and how best it can be part of the new data management implementation plan. Adhering to quality standards results in a culture of data quality that increases data value in the long run.

Step 4 – Governance policies for each component

Each component mentioned above must come under a governance policy to ensure a seamless enterprise data management experience across the organisation. Teams should study the system architecture and data flow diagrams in detail to get an overview of the captured data and its use by the company.

You must set Standards using ETL/ELT processes or data virtualisation to combine and make sense of data coming in from different sources. Policies control the quality and storage of data to ensure that it aligns with the objectives set out in the beginning.

Data security, master data management, analytics, metadata management, data warehousing, content management and reporting are the other components that ensure efficient data management when placed under a thoroughly thought-out governance policy. 

We have previously identified Microsoft Sharepoint and Doxis Enterprise Content Management as two of the most robust technology platforms for EDM. Using these platforms, we have been able to deliver the best solutions to diverse customers in different parts of the globe. 

The all-important implementation stage

Once data governance is fully defined, your organisation can move to the implementation stage of the enterprise data management solution. Determining governance policies will require the creation of a detailed roadmap for the entire project, along with sub-plans for each component mentioned earlier.

To begin with, the teams need to review the current technology stack and identify the engineering requirement for the data management service. The unit can outline the best architecture and features before finalising the optimal tech stack.

Planning the detailed scope, timeframes, and deliverables can be a great way to cut down on the time duration of the project. A defined scope can also ensure that cost overruns never happen. Start with designing a collaboration workflow between teams and create a project KPI suite to monitor the progress and ensure alignment with the data governance policies.  

Each enterprise data management component needs a technical solution for integrating with the IT infrastructure. Have an iterative development process to ensure agility while deploying the solution and follow it up with full-fledged testing and training.

For organisations with in-house development capabilities, onboarding a professional Data Management Consultant to streamline the entire process will be ideal. Their involvement in the project will cover all the aspects that we discussed earlier but will stop short of the actual development and deployment. Consultants will also be able to develop a detailed business case for the entire project that can give the management a clearer picture of the RoI from the whole exercise of creating a data management service.

Setting up the core team

Deploy dedicated teams for implementing enterprise data management at scale. The group is a collection of specialists who have in-depth knowledge about the components they are working on while keeping an eye on the project’s overall objectives. Here are a few of the key people you should have on your team before embarking on any data management service project:

Data Management Consultant

For some organisations, a data management consultant is the first person they hire for the project. The individual will communicate with different stakeholders and understand their individual needs. The consultant will play a central role in formulating the implementation strategy and will also work on the documentation and training once the solution goes live.

Program Manager

The Program Manager is the one person who oversees the execution of the project and acts as a bridge between the various teams involved. The program manager also monitors and manages the cost of the project and the various components within it.

DevOps Engineer

Ensures the seamless development and integration of the various components while monitoring the IT infrastructure and maintenance.

Data Engineer

Another key core team member, the Data Engineer, analyses data sources and fixes data quality issues as and when they appear. They are also responsible for maintaining the metadata data model and building data flows and other relevant data models for the enterprise data management solution. 

QA Engineer

The QA Engineer primarily works with the testing side of the project using a test strategy and evaluates each component. They are also responsible for planning the testing activities and recommending improvements to the solution.

Solution Architect

The team member is responsible for creating a technical solution architecture from the identified business requirement. They supervise the development team and recommend improvements in the tech stack for optimal performance.

Understanding the sourcing model

Earlier, we briefly touched upon onboarding a consultant for your organisation’s data management project. But this is just one of how different companies approach enterprise data management. Broadly we can employ three outsourcing models as per your individual organisational needs:

In-house 

In cases where companies believe in creating the data management service in-house, a set of specialists are hired and trained to build the solution from scratch. The advantage here is that it gives the organisation complete control of the outcome. The main disadvantage can be a longer deployment time with the cost of hiring, training, and maintaining an in-house team of specialists.

In-house + Outsourced

For companies with in-house development skills, an external enterprise data management consultancy will create the policies and frameworks required to kickstart the project. This can speed up things from the company perspective compared to the entirely in-house model. Still, it also comes with the risk of identifying the right consultant and creating a seamless workflow that enhances coordination with the internal and external teams.

Fully Outsourced

This is by far the fastest way to reach the deployment stage because an outside vendor will have the full responsibility to cater to the demands of the internal team and deliver the results. Outsourcing is the best bet for companies looking to move ahead with their data management projects rapidly. Conversely, complete outsourcing also comes with the risk of total vendor dependency and long-term monetary costs. 

The importance of the right tools

The success of a data management service or solution depends on the tools and technologies that go into building it as per your requirements. For every major component, there needs to be a team that understands these technologies in and out and helps integrate them or customise them as per the requirement at hand. As a leading player in the industry, we have a team that uses the most advanced tools for data integration like SQL Server Integration Services, IBM InfoSphere DataStage, Oracle Data Integrator and more. 

Our team also specialises in cloud platforms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform. We use tools like Microsoft SQL Server, Amazon Redshift, Google BigQuery etc. for data warehousing and Power BI, Microsoft SQL Server, Google Developers Charts etc. for data visualisation. Our team does custom development for enterprise data management using industry-standard programming languages like Scala, Python, Java, C++, R, and more.

While identifying the right vendor, it is imperative to understand their technical capabilities with such tools that help build the data management service. Undertaking an in-depth review of their strengths and abilities will go a long way in pinpointing the suitable vendor who can deliver the right solution within the correct time frame.

Arriving at a cost

A complex enterprise data management solution can come with its fair share of monetary investments from the company’s side. Considering the highly skilled workforce needed for such projects, overall costs can vary from company to company and market to market. There are multiple factors that are considered to arrive at the right price for building the data management service

Data maturity is a key factor that’s taken into account because it defines the level of effort needed to get the project off the ground. Data analytics and the complexity related to it is also another important factor for deciding the cost metrics. Quality metadata and data sources will also have an impact on the overall cost. Depending on other internal aspects like data security, volume, references, data types, structures etc., the price will vary for each project. You can always contact us for a free consultation and custom quote for your EDM project. 

Conclusion

Enterprise Data Management is a highly specialised domain that requires highly specialised skill sets to deliver the right results. Choosing the right vendor can be a make-or-break situation for any company that expects to drive RoI from its data management service. Choosing the right consultant or vendor with the right skills and track record is quite literally half the job well done for such projects. A fully rolled-out enterprise data management platform needs to work like a well-oiled machine to help an organisation achieve its goals on all levels. 

At Neologix, we have numerous years of domain expertise in creating custom solutions for a diverse clients in the UAE. If you are in the market to identify a vendor who can help turn such a complex project into a seamless and hassle-free affair, please contact us at info@neologix.ae or +971-521043226. We will be more than happy to answer your queries and work on a plan to deliver the right EDM solution that your business needs.

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