The Truth About the Enterprise Data Warehouse (EDW)

Making enterprise-wide decisions is frustrating when an organization is operating in a silo. There is only so much impact a company can have without a holistic view of the entire organization’s operations. To keep up with ever-demanding customers and changing industry trends, companies must be able to query any and all data simultaneously.

That said, many businesses are still using siloed data, which is not easy, efficient, or accurate when making data-driven decisions.

Welcome the Enterprise Data Warehouse (EDW): an environment that allows companies to harness all business data. After normalizing data from all business units, it’s possible to uncover unknown dependencies between projects, pinpoint risks and opportunities, and make strategic choices on behalf of the whole company.

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What is an enterprise data warehouse (EDW)?

An enterprise data warehouse (EDW) aggregates and houses data from all areas of a business. Instead of attempting to draw conclusions from multiple datasets specific to certain departments, an EDW provides businesses with organized data in one place. An EDW enables numerous stakeholders to run accurate reports, plan efficiently, identify issues, and forecast future success.

Companies that do not leverage an enterprise data warehouse might have their own “data warehouse”, but it is typically a set of fragmented data marts —subsets of data unique to individual business teams. Corresponding reports can gauge progress towards department-specific KPIs. For example, data marts help answer questions like, “What is the average ACV for X sales segment?” or “How many IT help tickets were logged last quarter?”

While data marts can improve the quality of particular business groups, they are not comprehensive. Performing analysis on a normal data warehouse does not illustrate interdependencies between departments and is not meaningful on an enterprise scale. Broad, organizational questions are becoming more and more difficult for companies to answer without an enterprise data warehouse.

Four benefits of a robust EDW

Clearly, an enterprise data warehouse offers more than a “normal” data warehouse, but what are the tangible benefits? Below are just a few benefits an EDW provides:

Integration with analytics software: According to Salesforce.com, 37% of businesses state that data analytics processes facilitate growth in their business. EDWs foster more robust analytics by integrating fairly seamlessly with analytics software. Reporting and dashboarding on company-wide KPIs becomes much more scientific when a full range of data is available.
Contextual analysis: An EDW shows and defines relationships between data points, offering information and context when analyzing the business as a whole. With an EDW, you can better predict how even minor adjustments can affect the entire company.
Storage and standardization: Unlike normal data warehouses with various disconnected datasets, EDWs gather and store massive datasets from all areas of the business. In addition to accumulating huge amounts of data, EDWs transform and translate data for accurate comparison. While oftentimes data sources seem unrelated, storing and standardizing data can surface key connections between projects that have considerable impact on business success.
Flexibility: The structure of enterprise data warehouses is malleable. Users might realize that a data model has changed, or data needs to be added or removed. These adjustments do not require an entire overhaul of the system‒‒EDW quality improvements are implemented easily. An EDW grows with a business, becoming progressively refined over time.

Ellie Mae: An enterprise data warehouse in the real world

All these benefits sound great, but do they actually work? Let’s delve into a real-world example of how an enterprise data warehouse helped Ellie Mae decrease its customer list from 100,000 inaccurate contacts to 60,000 quality prospects.

Ellie Mae automates mortgages for credit unions, other mortgage companies, and banks. As a result, its customers can maintain day-to-day efficiency and stay compliant.

Before Ellie Mae began its enterprise data warehouse journey, the company operated from siloed databases with over 100,000 contacts. Since Ellie Mae had made several acquisitions, many contacts within these databases were duplicative, incomplete, or outdated. Not surprisingly, Ellie Mae’s marketing campaigns, sales strategies, and other lead management processes started failing.

Inaccurate data prevented any truly informed business decision making. Deriving a 360-degree view of the customer, segmenting customers into correct marketing target groups, and understanding what new products customers need was virtually impossible. By building an enterprise data warehouse, Ellie Mae bundled together records for the same customer that came from distinct data sources to create a single source of truth.

With a unique identifier for each customer, Ellie Mae avoided duplication issues and produced multidimensional customer data to be consumed by their CRM tool, Salesforce.com, and by their marketing engine, Eloqua. Overall, the EDW shifted email delivery rates from 70% bounces to 80% successful deliveries, and direct mail had remarkably fewer returns.

What’s more, Ellie Mae’s enterprise data warehouse solution is scalable — the company plans to implement self-service data integration, predictive analytics, and real-time reporting in Phase II of their EDW project.

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Enterprise data warehouse solutions

Building an enterprise data warehouse from scratch is an arduous task. With the plethora of EDW solutions available, it is important to evaluate which one will be best for your business. A few major deciding factors for an enterprise data warehouse tool are: cloud vs. on-premises, vendor attributes, and overall company objectives.

Company objectives

The first step in assessing any EDW solution is to identify business use cases, requirements, and goals. Going into a decision blindly will increase risk of rework, or of opting for a vendor that is ill-equipped to deliver on expectations. A comprehensive list of company objectives will assist in comparing and contrasting features of various EDW tools.

Cloud vs. on-premise

Enterprise data warehouses can be on-premise or cloud-based. The general perception of on-premises solutions is that they are quicker and more secure than cloud systems. Since data does not reside on far-away servers like the cloud, some businesses believe that stakeholders can start their analysis faster with on-premises tools. On the other hand, cloud-based tools have lower start-up costs and are easily scalable. Both on-premises and cloud-based tools allow organizations to have complete control over profiles and permissions.

EDW vendor considerations

Once the cloud vs. on-premises decision is made, the company needs to pick an enterprise data warehouse vendor. To ease data migration, the new solution should be compatible with a business’s existing system, or at least support the data types the company is looking to store. By the same token, the EDW solution should maintain optimal performance as data accrues over time.

The EDW vendor should also be scalable enough to mature with database and business intelligence teams. EDW software with open source roots might be an easier transition for IT resources. Maintenance is a huge pain point with conventional data warehouses, so it is crucial to find a vendor that allows IT to make adjustments without disrupting day-to-day business.

The cloud and the future of data warehousing

For most organizations, the need for integrated customer success, billing, marketing, shipping, and product data is only going to intensify. On-premises solutions cannot keep up with this demand and are steadily declining in popularity.

Cloud solutions can store far more data by connecting to other cloud software, like CRM or marketing systems. Most cloud solutions have a high “up-time”, making them accessible and reliable at all times. Due to initial uneasiness about storing data on the cloud, cloud-based EDW providers are also hyper-aware and apprised of security protocols and incorporate them into new features.

The cloud has introduced a deluge of data sources, like Internet of Things, mobile, and social media data. As consumers interact with products directly, enterprise data warehouses have to capture that data and present it in a digestible fashion. In the future, cloud-based tools will fully support predictive analytics, artificial intelligence, and machine learning, thereby encouraging more real-time, agile decision making.

Getting started with an enterprise data warehouse

Combined with analytics programs, EDWs are valuable, actionable resource. Not only do EDWs allow businesses to make more holistic decisions, EDWs also makes data accessible in one place. A unified environment and governed self-service access empower stakeholders to run accurate reports, identify issues, and forecast opportunities.

Responding to customer behavior is vital to sustainability. By constantly augmenting cloud-based EDWs with new consumer data, businesses can make nimble decisions and ensure future success. Streamlining and validating the data contained in EDWs requires the help of products that govern, transform, and share data.

Talend Data Fabric is a single suite of applications for data integration and data integrity. Users can collaborate using a common integration platform, which simplifies scheduling, monitoring and management. Get ready to see the full picture of your business by trying Talend Data Fabric today.