Data integration offers a wealth of opportunities that allow organizations to implement new innovative services, simplify delivery of public services, reduce fraud and human error, and create massive operational efficiencies. But data integration does not come without challenges.
Without the right skillset, technology or strategy, the way you handle your data could be hindering your BI plans, analytics objectives, and innovation goals. The result, a stagnant organization, which fails to deliver new products and services.
But what can your organization do to avoid this fate? Well, you need to overcome these data integration challenges.
Data Integration Challenges:
- Your data isn’t where it needs to be
- Your data is there, but it’s late
- Your data isn’t formatted correctly
- You have poor quality data
- There are duplicates throughout your pipeline
- There is no clear common understanding of your data across the organization
Before we delve into the core issues, let’s first define exactly what we mean by a ‘data integration challenge’.
What is a data integration challenge?
A data integration challenge is something stopping you from achieving control over the processes and output of your data integration. It’s the obstacle preventing a single, unified view of your disparate data.
And what’s data integration? Data integration is the creation of usable data with consistent quality, from diverse data sets. It involves retrieving data from different sources and merging it to create a single, unified view. This unification makes it easier to catalyse insights and deliver faster, more meaningful organizational productivity and improved data products.
Getting on top of data integration challenges is important when processing data at scale and when working to mature your organization’s data strategy.
The Top Data Integration Challenges
Now that you have a broad overview of what the data integration challenge is, let’s look more specifically at a few commonplace examples.
Here are six data integration challenges your organization might face and some ideas on how to solve them.
1. Your data isn’t where you need it to be
You want your data in a centralized location, but you struggle with centralization. Sounds familiar?
This data integration challenge is commonly a result of depending on human power alone. Relying on developers to curate data from disparate sources and perform data wrangling takes time. This is time that your organization could use to spend on analysing data insights and driving valuable business practices. In order to speed up your innovation goals, it’s better to enlist the help of a smart data integration platform. This will do most of the heavy lifting. It’s a great way to say goodbye to some of your data integration issues.
2. Your data is there, but it’s late
Some processes require real-time or near real-time data collection. For example, if you’re a statistician, running a global trade site, you may choose to publish tailored, targeted metrics to each individual consumer based on their country and search history.
If the data isn’t collected in the timespan you expect, you can’t meet these demands. Unfortunately, relying upon your team to manually collect data in real time is impossible at best. The likelihood is, you don’t have the resources or employee power to undertake such a daunting task.
If you would like to push for real-time data ingestion and, consequently, innovative and reactive services, your only way forward is with an automated data integration tool. This technology will reliably curate real-time (or near real-time) data without having to sacrifice your resources.
3. The data isn’t formatted correctly
Disparate or anomalous data that’s incoherent or in the wrong format isn’t actionable – its value is lost. But manually formatting, validating, and correcting data is onerous and takes up a lot of your developers’ precious time.
Data transformation tools eliminate this problem by analysing the original base language, determining the correctly formatted language, and automatically making the change. This process takes the stress out of data integration and limits the number of errors, especially when your data team can flag and inspect code at any point in the transformation pipeline.
4. You have poor data quality
Poor quality data leads to lost productivity, missed insights and data not fit for purpose. That’s why data quality management is an essential part of driving new product innovation, staying compliant, and making more accurate decisions.
By proactively validating your data as soon as it’s ingested, you lower the amount of bad data entering your systems. In addition, you can also monitor your data pipelines for outliers and automatically spot errors before they become larger issues.
5. There are duplicates throughout the data pipeline
Organizations are increasingly aware of duplicate data in their systems, because of siloed legacy environments. While duplicates may appear harmless at first, they can cause serious problems long term, when data needs to be integrated. The more duplicates you have the more difficult to enrich or augment that data by incorporating values from other datasets. To help combat duplicates and eradicate data silos organizations should:
- Create a culture of data sharing between technical and business teams
- Standardize the validated data and ensure everyone understands it
- Invest in technology that encourage team collaboration
- Adhere to regulatory requirements that promote transparency, data privacy and security
- Track data lineage
6. There is no clear common understanding of the data
We’ve already discussed the importance of communication between technical and business teams regarding data sharing. But establishing a common vocabulary of data definitions and permissions is equally as important.
You can achieve this common understanding through:
- Data governance – this focuses on the policies and procedures surrounding your data strategy.
- Data stewardship – a data steward is an individual who oversees and coordinates your strategy, implements policies, and aligns your IT department with your business strategists.
The amount of data organizations create is growing faster than ever before and is a latent source of value to every organization. But, unless these six core data integration challenges can be mastered, you won’t get the most value from your applications, functions, and processes. Get things right, and you can accelerate digital transformation using data as the cornerstone of your growth and development.
So, take a step back, review your business goals, and identify which of these challenges is preventing you from accomplishing your objectives. With the right culture, mindset, and tools and skills, your organization can conquer even the most complex data integration challenges.
Our next blog will examine the tools and technologies that can be employed to achieve successful data integration.
