The Intelligent Data Warehouse

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How Master Data Management

makes business intelligence intelligent

The

Intelligent

Data

Warehouse

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The more data an organization generates, the more it relies on its business intelligence systems to assist corporate decision-making. And with data becoming an increasingly useful competitive asset, business intelligence has never been more critical to

the enterprise.

But even with sizable investment in BI software, data warehouses, and data integration projects, analyst teams still struggle to deliver accurate reports and on time. Why? When the data they’re working with is a mess, it slows them down and harms the accuracy of the reports they’re producing.

If you’re like most companies, inaccurate, inconsistent, and largely duplicated data in your operational systems creates an unreliable foundation for all your analyses. The result of which is a fragmented and imprecise view of your customers, suppliers, products, and employees.

That makes the reports – and therefore decisions stemming from them

– inaccurate and unreliable, too. And that’s bad news for anyone who works with or on those reports and data.

The promise

of intelligence

And that makes your business intelligence really not very

intelligent at all.

Informatica The Intelligent Data Warehouse 2

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Business leaders are impeded from making correct time-sensitive, strategic decisions because the reports they use are inaccurate.

Business analysts end up spending most of their time manually reconciling data, leaving them little to no time to appropriately analyze sales performances, revenue allocations, and risk mitigation.

IT practitioners and data

architects have to undertake massive, error-prone, manual projects to clean, correct, and standardize data, making change a hellish process. And major change – like mergers and acquisitions – becomes a tedious, time-consuming nightmare.

Compliance officers can’t submit reports to authorities with any confidence of accuracy. Which means the whole company is at risk of suffering massive penalties for completely avoidable, essentially administrative errors.

Bad data affects

everyone

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This eBook is about learning how to

prepare your data so your business

intelligence initiatives can deliver on

their promise. Because until they do,

your whole organization will continue

to slide down the slippery slope of

poor data turning into poor intelligence

turning into poor decisions.

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So how did your

data warehouse

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Your data lives in tens if not hundreds of systems. And because different users in different departments enter information about customers, accounts, and products into all these systems, the information ends up in different formats with different levels of accuracy. This makes the data a duplicative, inconsistent, and inaccurate hairball of information that your data warehouse is not equipped to untangle on its own.

Dealing with this is difficult enough in one country, but the madness is multiplied for global organizations. And because your BI tools weren’t made to handle this complexity, your business analysts end up having to pick up the slack.

Worse, the mammoth task they’ll end up undertaking doesn’t actually resolve the problem in any sustainable way. They’ll work well beyond their means only to rediscover the same mess the next time they have to make a report.

The data landscape in which your business lives

is nothing like the one your BI system was built

for. The result is that your data warehouse isn’t

prepared for the chaos your data represents.

In particular, it struggles with two critical issues:

The data comes from multiple sources

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Your BI system wasn’t built to know that there’s a difference between GE and General Electric company. Data inconsistencies in your source systems get propagated through to your BI system. When garbage data goes in, garbage reports come out.

The accuracy of your financial reporting relies on the ability to reconcile different customers even if their transactions were handled by different systems. Without the ability to accurately summarize your data, you end up with bad analytical (and accounting) practices.

Without a single view of your customers, sales and marketing suffer. Without a single view of your suppliers, your supply chain is inefficient. And without a single view of your products, your product launches are delayed.

Your business intelligence is supposed to be based on an authoritative view of the business you can trust; but when you can’t trust your data, you can’t trust your intelligence.

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How this mess

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Sales

In increasingly competitive markets, your sales teams need to know everything they can about your customers if they’re to stand any chance of holding on to them. When they don’t, the lack of information inevitably turns into a distinct competitive disadvantage. For instance, Natura, a cosmetics manufacturing and retail company based in Brazil, realized that its sales force did not have an accurate view of its relationships and interactions with customers.

Marketing

For your marketing organization to strategically allocate resources, it needs to be able to segment the customer base and prospects by a variety of attributes. More importantly, it needs to be able to track the success of marketing

campaigns.

Without high-quality data on channel partners, customers, and prospects, Citrix, a high-technology company, didn’t have an aggregated view of those dimensions across Salesforce.com, Marketo, and MyCitrix.com. As a result, they couldn’t tell how many leads were converting to genuine opportunities.

When you don’t have a single source of

information that you can trust, just about every

department in need of intelligence suffers.

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Human Resources

When HP’s systems contained fragmented and duplicated data about customers, partners, and sales representatives, human resources erroneously compensated sales people for commissions.

You can pay a high price for a disparate view of employee data. Especially when their compensation is directly linked to their impact on the business.

Finance

When a large biotech company needed to set contract rates for its government customers, it realized it couldn’t trust the view it had of their activity and requirements. This lack of access to accurate reports about the relationship directly hindered its ability to

re-negotiate rates.

From a broader perspective, when there are duplications and inconsistencies that don’t get reconciled, the integrity of your financial reporting suffers. And it doesn’t take long for that lack of trust to compromise the quality of your business leaders’ decisions – decisions that are invariably tied to their view of the financial health of the organization.

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Compliance

To comply with U.S. federal and state regulations, organizations need to be able to track their operations and then report on them. As a result, it will take an unprecedented level of data accuracy to avoid penalties and jail time.

For instance, Quintiles, a contract research organization that conducts clinical trials on behalf of pharmaceutical companies, has to continuously comply with regulatory scrutiny from the Federal Drug Administration (FDA). If they don’t, the FDA can severely delay the approval of drugs, which is very expensive.

While some industries are more keenly regulated than others, no industry is exempt from compliance reporting – and certainly no organization can afford to take these duties for granted.

Research and Development

ICON, another contract research organization based out of Dublin, needs to communicate the ongoing results of its clinical trial studies with its customers – pharmaceutical companies. The reports inform the companies about the efficacy of trials and alert them to any adverse events. An inaccurate, incomplete view of critical entities such as investigators, sites, and locations meant that the company could neither monitor the risk of clinical trials and track investigator performance, nor create accurate reports.

And so many more

From risk to revenue to sales and supplies, any analysis, reporting, or decision that relies on

accurate data will suffer if your BI systems aren’t crunching the right numbers in the right way.

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Why DI and DQ

were good ideas,

but not enough

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While critical to bringing together all those different sources into a single system, data integration is a one-way connection that brings your data into the data warehouse and leaves it at the behest of your BI system, which was not built to reconcile the data.

More important, the integration

is hard-coded. So any time the schemas change or the dimensions are updated, your ETL developers have to undertake a massive manual task. They have

to navigate what could be as many as a million scripts to make the changes and then track the lineage and history of those dimensions.

Setting up a data warehouse requires

justifiably relying on two crucial tools – data

integration and data quality. Unfortunately,

while offering significant benefits, these tools

don’t make your business intelligence any

smarter. Here’s why:

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In a bid to increase the accuracy and therefore the value of data assets, data quality tools are usually employed to cleanse the data before it reaches the data warehouse.

But cleansing the data before it reaches the warehouse means your source systems are still full of inaccurate, incomplete data. Since data quality software does not maintain a repository of all your good, clean data, anyone using those source systems is still relying on data that hasn’t yet been cleansed.

So two people looking at the same data (let’s say monthly sales revenue) will see two versions of the truth if they’re looking at two different source systems. And those two versions are dictated by how often the respective systems update, rather than the reality of how many sales were actually made.

All this means that, as vital to the integrity of your data warehouse as these tools are, there’s still a missing piece in your BI puzzle.

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Master Data

Management and

the intelligent data

warehouse

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While the data sources are integrated, the data itself is still riddled with duplications and

inconsistencies.

While the data is cleansed, the clean data isn’t actually being stored anywhere.

While the data can change, it takes substantial manual, error-prone work to both implement and record those changes.

So the missing piece in your intelligence puzzle

would have to resolve all three limitations.

To recap, let’s list the limitations that hold

your data warehouse back.

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It’s a single, integrated, conformed, cleansed,

automatically updating repository of the single

version of the truth – on tap.

Integrates the data into a single,

authoritative view of the dimensions you can trust and act on. It resolves all the inconsistencies and duplications that trip up typical BI systems (like figuring out that GE and the General Electric Company are one and the same).

Cleans your data regularly and automatically. It then stores all of that good, clean data in a central repository that updates all the source systems it’s hooked up to. So unlike your BI, which is a terminal system, MDM is capable of enabling bi-directional data flows.

If the data changes, it alerts you to the anomalies immediately so the changes can be inspected, stored, and tracked. This means you have a trustworthy view of the data’s history and lineage. And your ETL developers don’t have to work double shifts to accommodate all the changes before the data’s ready for use.

The data professional’s panacea

Master Data Management (MDM) is the controlled

process that brings you a code-free, low-maintenance

solution that does what your BI system, your DI tools,

and your DQ processes don’t do.

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Two data warehousing scenarios: on the left, the risk of inaccurate reporting when an MDM system is not used to create reliable master data before it is fed into the data warehouse.

On the right, how an MDM system supports accurate reporting by resolving the data issue before master data

is fed into the data warehouse.

How MDM delivers reliable

data you can trust

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“Our primary goal is to drive pipeline for sales

and we’ve been able to tie a 20 percent lead conversion

improvement with MDM. The holistic view of data

and the ability to perform predictive analytics will

give us almost fortune teller-like abilities.”

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The MDM system removes most

of the transformation effort that usually goes in to populating conformed data structures. So your data warehouse resources can focus on answering business questions rather than integrating data.

MDM resolves the performance

problems that typically arise when data warehouse developers adopt a slowly changing dimensions approach.

By providing a history-tracking option, it records all changes on a reference data entity so that the data warehouse can focus on tracking the data segmentation changes as slowly changing dimensions. And if a user wanted to query the history of a non-slowly changing dimension for a record, they could just drill through to the history tables in the MDM system.

Because it keeps detailed data lineage for every field on every record (down to the cell level), MDM eliminates the need for time-consuming, manual data tracking.

This means that rather than querying across all your staging tables to find out ‘why the customer name value for a record is John Smith,’ you can just access the MDM system and identify the source system from which the value came. It even tracks the history of record merges.

The MDM technical advantage

From a BI perspective, MDM’s greatest contributions

revolve around its ability to do three technical things

extremely efficiently:

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What an

MDM-fueled data

warehouse does

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Having a clean source of constantly

updating data going into your data

warehouse means your analytics and

intelligence systems are running on the

only kind of data they should ever have

to consume – reliable, authoritative,

and accurate data that represents

a single, consolidated version of the

truth, the whole truth, and nothing

but the truth.

That’s great news for everyone who

comes in contact with the reports and

data coming out of the BI system you

invested so much time and money into.

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Business leaders can make better decisions safe in the knowledge that their reports are delivering a crystal clear view of the whole picture.

Business analysts don’t have to spend all their time looking for errors and

inconsistencies – they get to do the job you hired them for (analyze the data). They can also validate their hunches more easily and test their hypotheses more thoroughly.

IT practitioners and data

architects can focus on enhancing the quality of the data itself, rather than the plumbing that’s delivering it. Also, they won’t be hard coding every time a schema changes because they know they’re working with a system that’s primed for continuous improvement and integration.

Compliance officers can produce reports with the level of accuracy regulators demand. More important, better reports empower them to enforce the standards they were hired to uphold.

The benefits abound with a business intelligence system that runs on accurate information you can actually trust. And

MDM plays the starring role in delivering your BI system to that Promised Land of genuine intelligence. If your business is to make more intelligent decisions, then your business intelligence has to work with better data.

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Our MDM product and its multi-domain capabilities have made waves in organizations of all sizes and industries of every kind. More important, it’s been positioned as a leader in Forrester’s MDM Wave report.

Read ‘The Forrester Wave: Master Data Management Solutions, Q1 2014’ (English-only report) now to find out how.

Mastering Master

Data Management

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About

Informatica

We’re Informatica and we’re helping organizations put data first by delivering complete, accurate views of business-critical data about products, suppliers, and customers.