Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    big data analytics in transporation
    Turning Data Into Decisions: How Analytics Improves Transportation Strategy
    3 Min Read
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: The Push and Pull of Data Integration
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Business Intelligence > The Push and Pull of Data Integration
Business Intelligence

The Push and Pull of Data Integration

EvanLevy
EvanLevy
4 Min Read
SHARE

In my last blog post, I described the reality of so-called analytical data integration, which is really just a fancy name for ETL. Now let’s talk about so-called operational data integration. I’m assuming that when the vendors talk about this, it’s the same thing as “data integration for operational systems.” Most business applications use point-to-point solutions to retrieve and integrate data for their own specific processing needs. This is ETL in reverse: it’s a “pull” process as opposed to a “push” process.Unfortunately this involves a lot of duplicate processing for people to access individual records from source systems. And like…

In my last blog post, I described the reality of so-called analytical data integration, which is really just a fancy name for ETL. Now let's talk about so-called operational data integration. I'm assuming that when the vendors talk about this, it's the same thing as "data integration for operational systems." Most business applications use point-to-point solutions to retrieve and integrate data for their own specific processing needs. This is ETL in reverse: it's a "pull" process as opposed to a "push" process.

Unfortunately this involves a lot of duplicate processing for people to access individual records from source systems. And like their analytical brethren, the moment a source system changes, there is exponential work necessary to support the new modification. Multiply this by thousands of data elements and dozens of source systems, you’ll find a farm of silos and hundreds (if not thousands) of data integration jobs. It's not an uncommon problem.

More Read

Our technology collects content from thousands of high-quality…
“Of those respondents who said their companies still make decisions based on judgment rather than…”
A Shortcut Guide to Machine Learning and AI in The Enterprise
More Marketing Agencies Utilize AI to Embrace Automation
WayIn Adds Another Dimension to Polling

In most BI environments we begin with a large batch data movement process. We build our ETL so it can occur overnight. But our data volumes are such that overnight isn’t enough. So the next evolution is building "trickle load" ETL. The issue here is that data integration is less about how the data is used as it is when the data is needed and the level of data quality. Most operational systems don’t clean the data, they just move it. And most ETL jobs for data warehouses will standardize the formatting but they won’t change the values. (And if they do fix the values, they don’t communicate those changes back to the source systems.)

If I have specialized data needs I should be building specialized integration logic. If I have commodity or standard needs for data that everyone uses, the data should be highly cleansed.

So it's not about analytical versus operational data integration. It's not even about how the data is used. It's really about one-way versus bi-directional data provisioning. As usual, the word integration is used too loosely. In either case, the presumption that the target is a relational database is naïve. And whether it's for analytical or operational integration is beside the point.

Link to original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ai for building crypto banks
Building Your Own Crypto Bank with AI
Blockchain Exclusive
julia taubitz vn5s g5spky unsplash
Benefits of AI in Nursing Education Amid Medicaid Cuts
Artificial Intelligence Exclusive News
AI role in medical industry
The Role Of AI In Transforming Medical Manufacturing
Artificial Intelligence Exclusive
b2b sales
Unseen Barriers: Identifying Bottlenecks In B2B Sales
Business Rules Exclusive Infographic

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Some of our top Business Intelligence tweets

2 Min Read
Image
AnalyticsBusiness Intelligence

Advanced Analytics Offers New Opportunities for Growth

3 Min Read

Data Governance? What’s That? (And How Can Companies Fix It?)

6 Min Read

Is analytics a winner in a recession?

5 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?