Besides the small introduction, subscribers and consulting clients within this management domain have access to:
Identify data integration pains and needs and use them to collect effective business requirements for the integration solution.
Determine technical requirements for the integration solution based on the business requirement inputs.
Determine your need for a data integration proof of concept, and then design the data model for your integration solution.
Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.
Explain approach and value proposition.
Review the common business drivers and how the organization is driving a need to optimize data integration.
Understand Info-Tech’s approach to data integration.
Current integration architecture is understood.
Priorities for tactical initiatives in the data architecture practice related to integration are identified.
Target state for data integration is defined.
1.1 Discuss the current data integration environment and the pains that are felt by the business and IT.
1.2 Determine what the problem statement and business case look like to kick-start a data integration improvement initiative.
1.3 Understand data integration requirements from the business.
Data Integration Requirements Gathering Tool
Understand what the business requires from the integration solution.
Identify the common technical requirements and how they relate to business requirements.
Review the trends in data integration to take advantage of new technologies.
Brainstorm how the data integration trends can fit within your environment.
Business-aligned requirements gathered for the integration solution.
2.1 Understand what the business requires from the integration solution.
2.2 Identify the common technical requirements and how they relate to business requirements.
Data Integration Requirements Gathering Tool
Data Integration Trends Presentation
Learn about the various integration patterns that support organizations’ data integration architecture.
Determine the pattern that best fits within your environment.
Improvement initiatives are defined.
Improvement initiatives are evaluated and prioritized to develop an improvement strategy.
A roadmap is defined to depict when and how to tackle the improvement initiatives.
3.1 Learn about the various integration patterns that support organizations’ data integration architecture.
3.2 Determine the pattern that best fits within your environment.
Integration Reference Architecture Patterns
Data Integration POC Template
Data Integration Mapping Tool
"Point-to-point integration is an evil that builds up overtime due to ongoing business changes and a lack of integration strategy. At the same time most businesses are demanding consistent, timely, and high-quality data to fuel business processes and decision making.
A good recipe for successful data integration is to discover the common data elements to share across the business by establishing an integration platform and a canonical data model.
Place yourself in one of our use cases and see how you fit into a common framework to simplify your problem and build a data-centric integration environment to eliminate your data silos."
Rajesh Parab, Director, Research & Advisory Services
Info-Tech Research Group
Data is one of the most important assets in a modern organization. Contained within an organization’s data are the customers, the products, and the operational details that make an organization function. Every organization has data, and this data might serve the needs of the business today.
However, the only constant in the world is change. Changes in addresses, amounts, product details, partners, and more occur at a rapid rate. If your data is isolated, it will quickly become stale. Getting up-to-date data to the right place at the right time is where data-centric integration comes in.
"Data is the new oil." – Clive Humby, Chief Data Scientist Source: Medium, 2016
To keep up with increasing business demands and profitability targets and decreasing cost targets, organizations are processing and exchanging more data than ever before.
To get more value from their information, organizations are relying on more and more complex data sources. These diverse data sources have to be properly integrated to unlock the full potential of your data:
The most difficult integration problems are caused by semantic heterogeneity (Database Research Technology Group, n.d.).
80% of business decisions are made using unstructured data (Concept Searching, 2015).
85% of businesses are struggling to implement the correct integration solution to accurately interpret their data (KPMG, 2014).
Integrating large volumes of data from the many varied sources in an organization has incredible potential to yield insights, but many organizations struggle with creating the right structure for that blending to take place, and data silos form.
Data-centric integration capabilities can break down organizational silos. Once data silos are removed and all the information that is relevant to a given problem is available, problems with operational and transactional efficiencies can be solved, and value from business intelligence (BI) and analytics can be fully realized.
Data has massive potential to bring insight to an organization when combined and analyzed in creative ways.
It is difficult to bring data together from different sources to generate insights and prevent stale data.
Answer: Info-Tech’s Data Integration Onion Framework summarizes an organization’s data environment at a conceptual level, and is used to design a common data-centric integration environment.
59% Of managers said they experience missing data every day due to poor distribution results in data sets that are valuable to their central work functions. (Experian, 2016)
42% Reported accidentally using the wrong information, at least once a week. (Computerworld, 2017)
37% Of the 85% of companies trying to be more data driven, only 37% achieved their goal. (Information Age, 2019)
"I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts." – Sir Arthur Conan Doyle, Sherlock Holmes
90% Of all company generated data is “dark.” Getting value out of dark data is not difficult or costly. (Deloitte Insights, 2017)
5% As data sits in a database, up to 5% of customer data changes per month. (Data.com, 2016)
"Most traditional machine learning techniques are not inherently efficient or scalable enough to handle the data. Machine learning needs to reinvent itself for big data processing primarily in pre-processing of data." – J. Qiu et al., 2016
1. Disconnect from the business
Poor understanding of the integration problem and requirements lead to integrations being built that are not effective for quality data.
50% of project rework is attributable to problems with requirements. (Info-Tech Research Group)
45% of IT professionals admit to being “fuzzy” about the details of a project’s business objectives. (Blueprint Software Systems Inc., 2012)
2. Lack of strategy
90% Of organizations will lack an integration strategy through to 2018. (Virtual Logistics, 2017)
Integrating data without a long-term plan is a recipe for point-to-point integration spaghettification:
3. Data complexity
Data architects and other data professionals are increasingly expected to be able to connect data using whatever interface is provided, at any volume, and in any format – all without affecting the quality of the data.
36% Of developers report problems integrating data due to different standards interpretations. (DZone, 2015)
Most organizations don’t have the foresight to design their architecture correctly the first time. In a perfect world, organizations would design their application and data architecture to be scalable, modular, and format-neutral – like building blocks.
Benefits of a loosely coupled architecture:
However, this is rarely the case. Most architectures are more like a brick wall – permanent, hard to add to and subtract from, and susceptible to weathering.
Problems with a tightly coupled architecture: