3 Complex Data Integration Problems Derailing Your Analytics Strategy (and How to Solve Them)
In Evanta’s recent Leadership Perspective Survey, nearly 900 CIOs indicated “data and analytics” as a top priority to ensure their enterprise goals are met.1 Lines of businesses feel almost equally strongly about the importance of analytics, but for these users sitting in marketing, sales, finance, etc., tapping into data as a critical resource can be difficult without help from central IT.
Almost every business generates petabytes of customer data across an ever-growing number of digital and physical channels. According to an Informatica survey, more than half (55%) of chief data officers report more than 1,000 sources of data at their organization.2 The good news is that in the digital-first era, there is no dearth of tools to support effective data analytics.
So, in theory, it should be easy to mine insights from the data to drive smarter business decisions that impact overall performance. In practice, however, that can be easier said than done.
The Whole Is Greater Than the Sum of Its Parts in Business Analytics
Collecting data is a start, but the real value comes with applying that data to make smarter business decisions. Customer data spread across multiple systems holds little value by itself. You need to connect your systems and prepare your data for advanced analytics to be able to unlock its full potential.
The process of extract, transform, load (ETL) or extract, load, transform (ELT), which plays an integral part in data integration, involves connecting different data source systems and standardizing different data formats into a common language to prepare high-confidence data that's ready for analytics.
The complexity comes in cleaning, connecting, moving, mapping, joining and standardizing vast volumes of data, sometimes at very high velocity, in different formats, spread across multiple systems. This is compounded for enterprises, where legacy systems co-exist with modern apps.
Data integration can address data complexity but trying to do it manually or with suboptimal data integration tools can be slow, costly and error prone. Ultimately, it impacts the quality and speed of your data analytics and causes a domino effect that can derail your efforts by:
- Clouding visibility: If you don’t know which channels and touchpoints are working, you could misallocate funds or fail to meet customer expectations.
- Lowering data confidence: Inconsistent or error-prone data leads to low confidence in analytics and overall data programs within the organization.
- Impacting campaign optimization: Without effective analytics, connecting with your customers can be challenging. It makes it difficult to optimize future campaigns or improve ROI.
- Creating operational inefficiencies: Data analysts spend more time wrangling, cleaning and preparing data than using insights to design the best campaigns.
- Lowering CX standards: Ultimately, poor data and decision-making lead to suboptimal customer experiences. Personalization and experiences developed based on inaccurate data erode customer trust and brand credibility.
Overcoming Data Complexity for Successful Analytics
Breaking down data complexity helps organizations become customer focused, agile and innovative. Here are three main challenges organizations frequently face and tips on how to overcome them:
1. The data connection barrier
Connections across data systems act as the source of customer insights. The better the connections, the stronger the insights. Imagine how difficult it would be for a doctor to make a diagnosis based on isolated symptoms. In the same way, businesses must connect the dots between data from diverse sources to get the whole picture of the customer to develop effective strategies to reach them.
In 2022, Deloitte found that only 28% of CMOs said they were able to integrate all of their customer data collected over multiple touchpoints.3 And on top of connecting all the sources of customer data, you also need to connect it with relevant data from other areas, such as sales, ERP, billing, logistics or service, to provide a truly holistic view of the customer journey.
How to break down the data connection barrier
Data practitioners tasked with generating insights need a smart data integration tool to connect their many data sources to a central cloud repository, such as a warehouse or lake. In the absence of an effective cloud data integration tool, data professionals in a hurry often end up hand-coding ad-hoc pipelines to connect sources and generate data to meet urgent campaign needs. This DIY approach to data integration is unstable, unscalable and often inaccurate or incomplete. It can even create data security loopholes.
Picking an ineffective data integration tool can be equally suboptimal and end up costing more in the long run by reinforcing silos or adding to complexity and cost.
Instead, start right with a proven, secure and stable data integration solution that makes it fast and easy for data professionals to create pipelines. That way, they can connect virtually all their data sources to a central enterprise data warehouse.
The no-code, cloud-based Informatica Data Loader offers a simple and free way to create reliable, fast pipelines between most data sources and all major cloud data destinations. Data analysts within different departments can easily load billions of rows of data in virtually any structured or unstructured format in minutes, without any technical expertise or help from central IT.
2. The data quality barrier
Once all data source systems are connected, you should be ready to run analyses. But at this point, data practitioners are up against a barrier because data comes from multiple systems in different formats with redundancies, gaps and other inconsistencies.
Even after connecting and loading, this data is often unusable without heavy-duty cleaning and prepping — a process known as data transformation. Using inaccurate data for analytics can lower user confidence in the insights.
Add to it the reality that all data has a shelf life, and you’ll see there’s not much time to waste between collection and analysis if you want to avoid data decay. And while speed matters, it should not come at the cost of data security and governance.
How to remove the data quality barrier
In our 24/7 digital world, customer data streams non-stop from multiple sources. Thus, one-off or ad-hoc data cleansing is inefficient. Enterprise data practitioners need an institutionalized, automated, ongoing data cleansing and transformation process which prioritizes factors such as data timeliness, completeness, consistency, accuracy, security and governance.
The Informatica cloud data integration solution is a true end-to-end ETL solution that delivers high-performance, high-speed and high-scale data transformation. This helps ensure virtually any data being fed into BI or analytics engines is cleansed, normalized and ready for use. Business users need a continuous stream of data in a consumable format that can be channeled to BI and analytics tools to mine insights.
3. The data usage barrier
Advanced data integration systems are often too complicated for your departmental teams to use optimally. But basic systems, while perhaps more user-friendly, cannot deliver the performance the enterprise needs.
While enterprise data practitioners shouldn’t have to choose between system capability and ease of use, more often than not, analysts are dependent on central IT for more advanced data transformations or more complex data analysis use cases. This dependence causes delays in data analytics that can derail time-sensitive go-to-market strategies.
How to eliminate the data usage barrier
An advanced data integration tool that is simple, fast and smart makes it easier for data practitioners to find, understand and use data. The ideal solution that busts typical usage barriers should be:
- Easy to deploy: Complicated systems require expensive engineering resources or access to overstretched IT resources. Both options do not work in these challenging times where businesses need to “do more with less.” But neither option expects data professionals to use their limited skills and time for coding, custom workarounds or patchwork fixes.
Instead, use a no-code system that is easy to deploy. For example, with Informatica CDI-Free and CDI-PayGo, data analysts can load and transform virtually any data, in any format, from any system, with a simple, no-code, wizard-driven experience and no technical expertise required.
- Easy and efficient to use: Most modern enterprise IT teams get a high volume of data requests. Departmental data practitioners cannot address these requests or do justice to them with ad hoc reports. Smart ELT tools such as Informatica CDI-Free and CDI-PayGo offer codeless point-and-click mapping interfaces to transform data, simple drop-down menus and repeatable pre-built data transformation templates to automate recurring tasks with consistent results every time.
- Easy to scale: As data maturity in the organization grows, you need systems to handle higher processing volumes and advanced use cases. If data integration systems don’t grow with the business, instability can disrupt or derail workflows and plans.
Informatica CDI-Free offers the ability to upgrade to the CDI-PayGo version for large-scale data processing, compliance protection and award-winning customer support, in a single click and without disruption. For a truly scalable enterprise-grade experience, users have the option to use Cloud Data Integration which helps with advanced transformations, offers Spark support and more.
- Affordable: World-class data integration capability shouldn’t be unaffordable for functional teams. Unfortunately, the high cost of data management operations often forces data teams into DIY solutions. Suboptimal tools too, which may seem affordable at first, often prove more costly in the long run. End-to-end ETL tools such as Informatica CDI-Free give data practitioners across the organization an advanced data integration solution for free, without the need for expensive developers and lengthy budget approvals.
Go From Low Confidence to High-performance Data Analytics
Departmental data teams may be overwhelmed by the data deluge, but with the right data tool, it’s easy to overcome typical barriers derailing data analytics outcomes. The key to freeing up time and resources from manual data wrangling while still giving departmental users the high-quality analytics they need lies in a cost-effective, fast and safe data integration solution.
With a proven solution from Informatica, departmental data teams can also win the confidence of central IT and finance while addressing data governance and affordability challenges.
Learn more about how to easily load, transform and integrate your data at www.informatica.com/free-paygo.
1Foundry, State of the CIO Survey, 2023.
2Informatica, CDO Insights 2023: How to Empower Data-Led Business Resiliency, 2022.
3Deloitte, The CMO Survey: Highlights and Insights Report, 2022.
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