Data Warehouse Best Practices and Implementation Steps

Are you looking for data warehouse best practices and concepts? In this post, DataArt’s experts in Data, BI, and Analytics, Alexey Utkin and Oleg Komissarov, discuss the entire flow — from the DWH concepts to DWH building — and implementation steps, with all do’s and don’ts along the way.
23/04/20
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By Alexey Utkin
Principal Solution Consultant, Finance Practice
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By Oleg Komissarov
Principal Consultant, Finance Practice
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Data Warehouse Best Practices and Implementation Steps

DataArt consultants have extensive experience building modern data platforms. We know first-hand that companies these days use software systems with varying technical and business requirements. Thus, there is no unified data warehouse (DWH) architecture that meets all business needs at a time. With this in mind, we’d like to share baseline concepts and universal steps that every team should follow to build a data warehouse that brings real value. 

What Is a Data Warehouse?

DWH is a centralized data management system that consolidates the company’s information from multiple sources in a single storage. This data is further used to draw analytical insights about the company’s performance over time and to make more substantiated decisions. DWHs, developed following modern “all things data” design patterns and cloud best practices, enable business intelligence (BI) services and unlock analytical capabilities that transform an organization into a truly insights-driven one. For instance, DWHs are put in the driving seat for data science and advanced AI or big data analytics.  

In this post, we will discuss data warehouse design best practices and how to build a data warehouse step by step — from the ideation stage up to a DWH building — with the dos and don’ts for each implementation step.

Step 1: Decide Whether You Need Outside Help

Companies that want to implement cloud-based data solutions (DSs) do not usually have enough expertise to do so, simply because such platforms are not standard IT or tech projects. Internal IT departments shoulder the responsibility of building a solution and, in the end, frequently fall short of expectations. A knowledge gap leads to high expenses and collapses in a cloud solution that is merely a replica of the previously used on-premise solution, with all its limitations and “skeletons” inherited.

The best approach to data warehouse development is to combine the efforts of in-house IT specialists who know all the internal business processes and external consultants who can facilitate the migration process. This collaboration may considerably reduce both development and infrastructure costs. Besides, it allows the company to make conscious choices: how to design a data warehouse step by step, how to make it more reliable and future proof.

Don’t: Try to build a solution with insufficient expertise, by relying solely on internal resources. This led many companies to cross their budget limits. Moreover, the result of amateur work is unlikely to meet the expectation of the company’s CTO or COO.

Do: Get ready to look for a consultant who is specializing in building mature DSs and who knows which architecture pattern will best suit your business needs. What if your company does not require a DWH at all?

Step 2: Outline Your Strategy and Tactics

Prior to building a solution, the team responsible for this task has to determine the strategy and tactics required, based on corporate business objectives. It is critical to capture and communicate the results that business stakeholders want to see in the long run.

Among a few recent clients’ projects at DataArt, we see one or a combination of the following high-level strategic drivers prevailing when implementing modern data architecture:

  • Enable insight-driven organization, or giving business users a combination of traditional BI and reporting workloads, with self-service and agile BI and ad-hoc querying, while addressing traditional challenges of data integration, governance, and quality.
  • Enable next-generation data products, data-driven apps, embedded BI, and data delivery APIs. In a way this is similar to the first driver, yet focused on external clients.
  • Enable advanced analytics: address the needs of data scientists and engineers, and implement use cases powered by real-time analytics and machine learning.
  • Re-platform, often with cloud technologies, to improve scale and reduce the cost of infrastructure, implementation, and maintenance of your data analytics solution.

Generate a structured plan, including the objective metrics that business stakeholders want to achieve along with every data warehouse building steps. This may be the speed of solution deployment, cost performance index, time to market, or combating legacy challenges in data platforms.

If you omit this step, your data warehouse implementation is likely to fail for one of these reasons:

  • The business needs and reality change much quicker than you can develop your DS.
  • Your business is unable to accept, process, and adjust to multiple changes at once.
  • Your new solution is not what is really needed because of a lack of frequent feedback from key business users.

Don’t: Rely on Big Bangs. Moving directly from the idea of a DWH solution to its development carries lots of drawbacks, such as a long time to market, low solution capacity, and lots of money spent in vain.

Do: Start with the business value the data platform brings, iterate, and evolve gradually as more and more feedback from end users is collected. Your team has to generate an envisioned, specific successful business scenario, based on dialog with decision-makers, the company CTO, and/or COO, and only then should you move to another step in the journey.

Step 3: Find Stakeholders Committed to the Project

Managing the entire process of integrating a DWH solution with corporate-wide resources is exhausting and time-consuming. The knowledge gap in the expertise of your IT team, along with an unclear vision of the future project, is a key blocker in the implementation success of the future DWH.

When you have outlined your strategy and tactics, gather a team of stakeholders who express the same level of interest in your project, would be using the DWH in the day-to-day activities, and commit to its success. These would not necessarily be C-level stakeholders in your organizations. Most often, end-users of a DWH are data scientists, engineers, and business analysts. 

Don’t: Initiate the project if you see that stakeholders are not committed to positive changes and do not contribute to the success of the DWH project.

Do: Find a committed group of stakeholders who have a clear benefit from and interest in the project’s success. Allow this group to facilitate the DWH development process and be the early-adopters. Preferably, this team should include business decision-makers, tech leaders, and analytics champions (e.g. CDO), along with the end-users of the solution.

Step 4: Perform a High-Level Assessment of Your Current and Target States

At this stage, your task is to think over appropriate methods for evaluating the effectiveness of data warehouse implementation for your business and create an elaborate vision of a specific successful business scenario. This means you must understand whether the DWH concepts fit your existing technological landscape and whether building a data warehouse meets your long-term expectations.

Don’t: Launch the project without knowing how to assess its success in the future. Simply building and integrating a DWH does not suffice.

Do: Identify metrics to measure DWH implementation success, performance, and adoption by all departments in the company. These metrics may include, but are not limited to, the speed and scale of data processing, data volume it supports, and how fast new inputs and analytics use cases can be introduced, at least for the group of early adopters.

Even more importantly, the company should envision how end-users will engage with the future DS, and whether it would bring benefit to their daily scope of tasks. Afterward, it is useful to digitize these indicators in order to rely on them while planning a potential data model and analyzing efficiency.

Step 5: Decide on Data Warehouse Concepts and Tools

With an exploded set of technologies, it has become difficult to decide how to build a DWH technology-wise and identify which tools to use for this project. Thus, before choosing a technology to build your modern solution, you need to understand the range of alternatives to choose from.

Looking to modernize your data platform?

By relying on three of the four big data Vs (Volume, Variety, and Velocity), you can distinguish the following platforms:

  • DWHs are optimized for structured, cleansed, and integrated information and target a wide range of business users.
  • Data lakes (DLs) are used for unstructured raw data, where volume and variety of inputs matter. These solutions let you store and process information in a low-cost and scalable way. DLs are used more by sophisticated business data analysts, scientists, and engineers.
  • To support data velocity and provide real-time analysis, implement streaming analytics solutions, which may use the technology similar to DLs, but are specially configured to hit the required latencies.

Depending on your type of information and its usage, you have to choose the appropriate technology solution, or – more often – adopt a hybrid solution. If you are still not sure which architecture to use, watch our recent webinar, “DL vs DWH” and learn how to modernize your data management and analytics platform.

Another approach to DS concepts is to distinguish them by the workloads they address:

  • Traditional BI and reporting workloads are covered mainly by structured data from DWH. Here, the team of data engineers is responsible for sourcing, integrating, and modeling of data, development of reports, dashboards, and data marts. This approach is time-consuming and expensive but well justified for the most important organizational data being used by a wide group of business users, including CxOs and senior management.
  • Self-service BI allows business users to perform data sourcing and aggregation, as well as reporting and dashboarding. In this case, a team of data engineers and analysts may monitor and support this solution and serve business users.
  • Ad-hoc querying allows business users to source data and query a wide set of available data, often unstructured and stored in different systems.
  • Data science workloads cover the needs of data scientists, such as querying big data and the use of data science tools.
  • The machine learning production pipeline supports models created by data scientists for self-studying, self-monitoring, and self-adjusting.

Snowflake, Oracle Exadata, Teradata, Microsoft Parallel DWH, and AWS are among the top cloud-based DS providers that can facilitate any of the above data types. 

Don’t: Choose a solution without understanding whether it suits your specific business needs and use cases, whether it is cost-efficient, and whether it provides sufficient scaling and flexibility.

Do: Choose the cloud solution, technology provider, tools, and concepts based on your type of corporate information and your business needs, to avoid incompatibilities.

Step 6: Validate Your Solution with an MVP

Building a minimum viable product (MVP) before kicking off a long-term project is one of the data warehouse best practices. Move forward by generating a simple MVP to demonstrate your DS functionality and engage with users to get real-life early feedback. This is a budget-optimal way to understand the real potential of the solution for your organization.

Don’t: Rush into a long-lasting project to build a DWH in one shot. 

Do: Demonstrate all the benefits of the future project through a simple MVP. Сreate a PoC to design and validate the elements of your solution.

Step 7: Create a Scaled Deployment Roadmap and Evolve Your Solution

The next step in your journey is to generate a roadmap with all project delivery points and metrics included. Good DS implementation approaches take into account three threads: incremental implementation of business use cases, increments of architecture and tooling foundation, and gradual business adoption of the new data capability and operating model. Once the roadmap is ready, start building your DS. At this point, it would make sense to work in partnership with an experienced consultant who can share their knowledge and experience with your team.

Don’t: Neglect the consultant’s assistance and the chance to learn from their experience.

Do: Try to learn from your technology partner and invest in relevant team education to stick to the latest technology news and trends on the market.

Step 8: Monitor and Optimize

In the old days, the data platform capacity was planned before its functionality was deployed for the end-users. But in the modern cloud and self-service reality, this could happen just after deployment. And it should happen anyway.

Don’t: Once your data platform is deployed, do not leave it without control. Otherwise, storage and computing costs may grow exponentially.

Do: Regularly monitor your platform workloads and pipelines to identify whether your solution needs any modernization or cloud spending optimization.

Conclusion

The entire process of integrating DSs may seem very resource- and time-consuming. Most companies mistakenly think that it will take months to implement a DWH for their business needs. In reality, by following DWH standards and best practices and with the right process facilitation, you can benefit from the first results in just weeks.

We hope you will find the data warehouse implementation steps we described useful for your business setting. If you need additional information or consultation, feel free to contact the DataArt team for more help.

FAQ about Data Warehouse Implementation

How Does Data Warehouse Work?

The data from multiple sources is consolidated in a DWH. When ingested, the data is cleansed and normalized, and then put into a dedicated database – depending on its type, format, and other characteristics. Data scientists, engineers, and business analysts use BI and other analytical applications to retrieve historical data from these databases in the format that suits their needs.

What Is a Data Warehouse Example?

Metaphorically, a DWH could be described as a beehive: it consists of multiple combs (databases) that are being constantly refilled by fruit nectar and pollen (information) collected by bees on different neighboring fields and meadows (a variety of input sources). 

What Are the Benefits of Data Warehouse?

DWH standardizes and stores valuable historical inputs about a company’s performance, which could further be used for more informed strategic decision-making, enhanced business intelligence, and, ultimately, generating higher ROI. 

What Factors Should be Considered While Designing a Data Warehouse?

Business requirements and use cases dictate the design of a DWH. Of course, the DWH should not interfere with the existing data collection and storage framework in the company.

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