Prepare Your Data forBusiness Analytics & Intelligence

How Master Data Management Reduces Riskin Any Data Warehouse Project

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Business Intelligence and the Risk of Bad Data The bottom line of business analytics and intelligence (BI) is data: getting the right information in the best format that allows a person to understand it and make an actionable decision quickly. This is especially critical when using data from multiple sources like an ERP, CRM, warehousing or manufacturing system. Through a set of tools, technologies, and best practices, BI solutions help make sense of your data, providing meaningful information and visibility from a combination of sources to your business users.

Accurate data is a non-negotiable cornerstone of any BI or reporting effort. When a company installs a BI solution, none of the inconsistencies in their data should be highlighted. Yet, more often than not, a company will go and try to build a dashboard - only to discover the numbers are wrong. One of the most common examples is a sales order that is posted without being attributed to either a customer or a salesperson and not checking against returns or credit memos. Revenue is misrepresented, and your data quality issues are the root cause.

Many kinds of enterprise data exist in an organization. Transactional data is made up of business events, supporting the daily operations of an organization. Analytical data includes the performance metrics and measurements that support decision making and reporting. Master data is the glue that represents the key business entities that execute transactions, and the dimensions upon which analysis is conducted (more on that later). The quality of each component and how you manage it inside your enterprise system is integral to the success of your BI project implementation. Otherwise, you’re making decisions based on bad data.

Trust Your Data with Master Data Management (MDM)PG. 4

Organize YourDynamics Data with a Data WarehousePG. 5

Dimensional Modeling and the Kimball MethodologyPG. 6

Simplify Data Management with Jet Data ManagerPG. 8

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Bad data is the biggest risk you face when embarking on an enterprise BI project.

The trouble is, you don’t necessarily know you are using bad data until it’s too late. Here are some of the major risks of using bad data in your BI solution:

• Multiple versions of the truth Questioning the validity of your data defeats the purpose of having a BI solution to make critical business decisions quickly. When you have the same reports telling you different results or inconsistencies in numbers, what can management really trust?

• Compliance and regulatory risk Inaccuracies in your financial and accounting reporting is one of the biggest, and more costly business risks of bad data. Submitting inaccurate data can result in huge fines and major repercussions for a business.

• Lost productivity and process inefficiencies Reviewing and analysing where the data went wrong takes the time, effort and patience of one or more employees. It also often results in complex workarounds and band-aid solutions to try and see the numbers they need.

• Slow time-to-value If you need BI, you usually need it right now. The resources spent fixing the problems that arise from bad data slows down adoption and prevents business users from seeing the value of BI.

• Inability to grow and scale without exponential resource consumption When you’re dealing with inconsistent reporting, complex workarounds, and delayed approvals, it’s not going to get easier when you add more data and processes to the mix. You end up spreading your resources thin and causing more pressure than before.

Without the proper data quality and master data management, these risks ultimately result in BI implementation failure. Business users will face challenge after challenge of finding value or usefulness in the BI platform, and it will end up eating away at your productivity, ability to service clients, and ultimately, your revenue. When done right, business intelligence with the right data quality and master data management processes in place will give you an effective BI environment and data governance roadmap to be used as a foundation for all your analytics and decision making.

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Trust Your Data with Master Data Management (MDM)

Accurate data doesn’t happen by itself. Data needs to be governed to a state of truth and reliability. At Jet Global, we treat BI as a data management project. In order to avoid any bad data risks and set your BI project up for success, it’s important to understand how to manage your data and ensure consistent, accurate data quality. Your data should be trusted, your people should be accountable, and your processes should be consistent.

Your Dynamics ERP system does a great job at collecting valuable data. As a result, you have a lot of it - but no guidelines in place to monitor it for accuracy and structure. There are two data management best practices that we always share with our clients before approaching the subject: master data management (MDM) and data governance.

Harmonizing and synchronizing multiple data items is extremely important in creating a single version of the truth, which is what you want your BI solution to be based on. Your master data is the specific set of identifiers and attributes that describe the core entities of your business, including customers, prospects, suppliers,

chart of accounts, etc. MDM is a strategy that uses those critical entities to establish a uniform, trusted view across an organization. Typically delivered through a hub infrastructure, all data sources, applications, and business processes map to your master data to ensure consistency, accuracy, and accountability.

Data governance is a set of processes that ensures important data assets are managed properly throughout the enterprise, encompassing data quality, data management, data policies, business process management and risk management, all in the context of a company’s information records. Through data governance, organizations are really looking to answer the deeper systemic questions about people, process, and methodology. With those answers, they put themselves in a position to exercise positive control over the processes and methods they use to handle their data.

Using both MDM and a clear data governance strategy will guarantee your important data assets are organized, managed, and controlled properly throughout the business with rules, regulations, and compliance.

4 Foolproof Steps to Implementing Data Governance

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Organize Your Dynamics Data with a Data Warehouse The basic goal of a BI solution is to provide a method of retrieving consolidated data to answer the business questions of your business users. Once you have your data governance and MDM plan in order, you need centralized data storage that allows you to migrate data and integrate it from multiple sources to give you that one version of the truth. With one view of your data, you are able to create accurate reports that you can then make smart, informed business decisions on.

To build a solid foundation for your BI solution, you need three types of databases in place:

1. Online Transaction Processes (OLTP) Database – this is what industries work within on a daily basis that supports the operations of the business including building orders, creating invoices, and tracking inventory. EX: Dynamics NAV database

2. Dimensional Database – this is constructed following the practice of dimensional modeling design and is built to quickly and easily get data out of a database. EX: Data warehouse

3. Online Analytics Processing (OLAP) or Tabular Model Database – optimized for analyzing large amounts of data in a timely and flexible manner using measures and dimensions.

It’s important to understand that each of these databases uses a different set of standards and can be very difficult to convert from one type to another. An OLTP database, like Dynamics NAV, is heavily normalized in structure, where data is spread across many tables. Most modern ERP systems are structured in this method because it allows data to be written and updated in a way that will minimize impact to other tables in the database.

When building a BI solution, it is most efficient to denormalize the data in an OLTP database. This is done by combining information from multiple tables in the normalized database into a set of smaller, consolidated number of tables that share a common subject. This is called a dimensional database, or data warehouse, which should be used to organize your data in the backend. It allows you to retrieve your data quickly, produce accurate information from it, and achieve the number one purpose of BI – to answer your business questions.

While your ERP system is used to collect data, a data warehouse acts as a single reporting engine to get organized, logical, and governed data out of your systems and into an easily digestible format for business users. Before you jump into any BI initiative, it’s important to understand why and how data warehouses work. This will help avoid any bad data scenarios and ensure you have a successful BI implementation and adoption rate.

Avoid 3 Common MistakesThat Cause Business Intelligence Project Failure GET WHITE PAPER

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Dimensional Modeling and the Kimball Methodology It all starts with dimensional modeling – the process of restructuring data that facilitates both performance and ease of use in reporting and analysis. ERP systems are relational databases, which means they focus on two types of data: facts and dimensions. These two types of data co-exist in your ERP system’s tables, but to enable easier reporting, a determination must be made as to how those items relate to each other. Generally speaking, data of each type is split within a BI solution into fact tables and dimension tables.

While there are a few schools of thought in dimensional modelling, one of the most popular and widely accepted approaches to data warehouse design is the Kimball Methodology. The core of this methodology is the need to gather business requirements from the people who will be using the system for analysis and reporting. Without understanding the needs of your audience, it will not be possible to deliver a BI platform that will meet their expectations. Furthermore, if the business users do not find usefulness or value in the BI platform, they will not use it and the BI initiative will fail due to a lack of adoption by the organization.

Every BI engagement that follows the Kimball Methodology starts by having meetings with key business users to uncover their needs, define key metrics, and discuss existing reporting issues. Once the needs of the business users have been identified, there are four fundamental steps to the dimensional design process:

1. Select the Business Process Any event that generates valuable business information is clearly identified. This includes any activity, from posting an invoice or entering payroll information to receiving a shipment from a vendor or applying a payment to an invoice. These events will be used to provide performance metrics and measurements in a fact table.

2. Declare the Grain The granularity, or grain, of a business process is used to determine the relevant dimensions and facts. The grain of a fact table establishes what each record or row in the fact table represents. Atomic grain level is strongly encouraged in most cases because it ensures that all details of the business process can be made available to the business users without redefining the grain.

3. Identify the Dimensions Dimensions are the who, what, where and when of the analysis surrounding business processes. They typically represent textual information and are structured in the form of a key field or fields as well as descriptive attributes about the key. An example would be based on the [Customer] table where the key field is [No.] and the dimension attributes are [State], [Country Code] and [Salesperson Code]. NOTE: Dimension tables will only contact Master Data that gives detailed context to the facts.

4. Identify the Facts Facts are the quantitative information generated by a business process. Numerical in nature, facts are extremely

important to the business users as they will comprise the metrics for performance measurement.

As an example, a meeting with a group of business users determines that they need better visibility into sales. One of the business processes that is selected is the process of invoicing customers. Now that this specific process is selected, it allows the group to focus on the level of detail that they wish to analyze data by (grain), the different contexts or manners in which they would like to analyze data (dimensions), and what metrics and values that are generated during the invoicing of a client are important to them (facts).

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Microsoft Dynamics Business Intelligence:Buy or Build?

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A data warehouse is critical to getting one complete view of your data. Not only does it form the source for accurate reporting, compliance, and decision making, it also allows you to migrate legacy data easily, so you don’t lose any historical data in the transition.

BI implementation projects are often an exercise in setting priorities. When it comes to developing a data warehouse, some companies decide to skip it, thinking they will save time on the extensive up-front analysis, design, coding, testing, and documentation required. There are many cases where choosing a pure front-end BI solution, like Tableau or Power BI, might do the job you need it to. But, as the volume, diversity, and complexity of your data increases, the cost and resources of consolidating all those data sources adds up and puts you at a high risk for bad data.

The drawbacks of relying solely on front-end BI tools for business analytics ultimately boil down to four areas of concern:

1. Speed

2. Performance issues

3. Data integrity

4. Longevity

The time it takes to plan, setup, and retrieve the right data from multiple data sources is slow and highly inefficient. The impact of running queries against core operational systems causes performance issues for both ERP and BI users. The risk of errors, data corruption, and the inability to identify the reason for the mistake compromises the integrity of the data and analytics you produce. And finally, any changes or upgrades made in any of your source systems or BI applications can have a devastating effect on your people, processes, and analytics.

A well-designed data warehouse will result in easily accessible data, more valuable reports, reliable and consistent data, improved security, and performance improvement. Not developing a data warehouse for your BI solution is not really a choice when you weigh the pros and cons. The good news is, there are choices when it comes to building that data warehouse. You can choose to plan, design, and build in-house or you can purchase a third-party solution with all the time-consuming programming and development already done for you.

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Simplify Data Management with Jet Data Manager Getting information into a place where a person can make it meaningful is the concept that built Jet Global. Since 2002, we have been delivering the best reporting and analytics solutions exclusive to Microsoft Dynamics customers. Our approach to migrating and managing data is simple and seamless. We apply the best data management and design principles to ensure our clients have better access and control over their data and we use the right tools to speed installation and time to value.

We offer a SQL-based BI solution called Jet Analytics, a complete data warehouse automation and BI customization platform 10X faster than manual coding - in case you were thinking about building your own BI solution. Jet Analytics uses the Jet Data Manager as a back-end tool to create data warehouses and cubes. This ETL automation tool is used to extract, transform, load, and report data into a final location. Here is a simple visualization of a basic BI project within the Jet Data Manager:

The ETL model is a basis for data flow in many data systems. In the Jet model, the data source is a database from which data is selected and extracted. The staging database is where the data gets transformed. And the data warehouse and cubes are where the transformed and aggregated gets loaded.

[Figure: Data flow through Jet Analytics]

Data SourcesNAV

OLTP dBase

StagingSelect / Transform

WarehouseLoad

Cubes ReportingPivot Charts, Jet Reports, KPI

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Jet Data Manager is a quick and efficient way of organizing and consolidating your data sources because of how we approach MDM. During a Jet Analytics implementation, a client will choose their master system (i.e. Dynamics NAV) and all other data sources, including the data warehouse and cubes, map to the master. Your master data essentially becomes the law for reporting. While not every piece of data within an organization will make its way into your BI environment, there may still be numerous places to pull the data from. The Jet Data Manager can connect to a wide variety of data sources to make this process structured and easy.

This approach to building a solid BI environment is designed for quick, accurate data access and optimal decision making. Here are some of the major benefits that our current Jet Analytics clients have experienced:

Jet Analytics is a complete BI solution built for Microsoft Dynamics that includes a pre-built data warehouse, OLAP cubes or Tabular Models, and dashboards, so you can begin to use it (and see the value) within a couple of hours. In addition, Jet Analytics has a complete real-time reporting environment built in, so you can consolidate your tasks into a single, proven platform used by over 154,300 Dynamics users. This also gives you the unique advantage of being able to report from either the live production database or the optimized data warehouse environment, giving you another dimension of flexibility in reporting while still enjoying the full power of complete analytics, dashboards, and BI. For more information, check out our website or get in touch with our team!

Data warehouse and cubes already pre-built for you – no coding or cube development required!

Not reliant on a small group of specialized resources that put the entire project at risk

Quick installation, rapid time to value

Low cost of ownership Produces governed data that the business can rely on

Creates one central repository for accurate reporting

Built to fit Microsoft Dynamics ERP structure Builds on Microsoft Stack Technologies

About Jet Global Jet Global delivers complete data access and control from Microsoft Dynamics so board members, executives, finance professionals, and managers can make decisions at the speed of business without the need for bottlenecked technical resources, or data expertise.

Through flexible reporting, fast analytics, and controlled budgeting solutions that leverage Excel and Power BI, Jet Global empowers users to become instantly successful and productive in an environment that is both user-friendly, familiar, and secure. Jet solutions focus on reducing the ongoing tension between IT and stakeholders by liberating the consistently over-tasked technical resources, allowing them to focus on innovating and driving the strategic aspects of the business. Visit www.jetglobal.com to see why 14,210 companies rely on Jet Global for their business insight every day.

Visit www.jetglobal.com to see why 14,210 companies rely on Jet Global for their business insight every day.

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