The Importance Of Data Integration For Successful Analytics
There’s one thing worse than having a lack of information, it’s having an abundance of inaccurate, outdated or useless information that sits in silos across the business, each with its own formatting standards that need to be reconciled. Each source of data contributes to a pool of data that tells a story about what’s going on across the business when viewed as a whole. However, Aberdeen’s recent Data Integration report reveals that IT teams are challenged in accessing this data to support enterprise analytics, pointing to an underlying need for better data integration processes to remain competitive.
Analytics Demands Better Data Access
Most businesses are now accessing on average 35 unique data sources according to Aberdeen Group, however sadly 30% of these were deemed “inaccessible” by the people that need to work with them. This coupled with the fact that organisations are using increasing amounts of non-traditional data, including third party data, location data, unstructured or text-based data and device and IoT data, creates a unique set of challenges for IT teams. With all these different data sources contributing valuable yet very different information, the need to allow access to all of them simultaneously is becoming more and more pressing. The form of data is more varied and continues to be scattered amongst disparate systems, making it difficult to consolidate for analytics purposes.
In fact, in Aberdeen Group’s 2019 Data Integration Study, Data Scientists believe their biggest hurdle is difficulty accessing data from different areas of the business while IT teams believe insufficient IT resources to support analytical initiatives are their biggest challenge. While both cite an increased urgency for fast information delivery and analysis. Industry leaders that have already accepted data integration as the key to a brighter future were seen to have two things in common: they can access their different data sources with ease, and they have the processing power, either locally or in the Cloud, to make use of all of their data at once.
What Is Data Integration? It is essential to realise data silos can result in operational inefficiencies, which can impact business growth and agility. From inaccurate reporting to wasted money and resources, siloed data can lead to poor decision-making, impacting on efficiency and profitability.
Part of data management, data integration is about combining data from disparate sources whether internal or external data to transform into meaningful and valuable information. In essence, data integration produces a single, unified view of a company’s data that a business intelligence application can access to provide actionable insights based on the entirety of the organisation’s data assets, no matter the original source or format.
Extract, Transform & Load (ETL) forms part of this process. As the name implies, ETL works by extracting data from its host environment, transforming it into some standardised format, and then loading it into a destination system like a data mart or data warehouse for use by applications running on that system. The transform step usually includes a cleansing process that attempts to correct errors and deficiencies in the data before it is loaded into the destination system.
Better Data Integration Is Better For Everyone
Data integration works across your organisation to support any number and type of queries — from the most granular of questions to the highest overarching concepts. Data integration can be applied to many specific use cases that impact every team and department in your business, including:
Business intelligence - Business intelligence (BI) encompasses everything from reporting to predictive analytics to operations, finance, and management. Further, it relies on data from all over your organisation to uncover inefficiencies, gaps in process, missed revenue opportunities, and more. Data integration supplies the BI tools and technologies your company is already using with the data streams your teams need to make their next big strategic decisions.
Customer data analytics - Knowing who your customers are, what behaviours they exhibit, and how likely they are to stay loyal or look elsewhere is paramount to good business. Data integration allows you to pull together information from all your individual customer profiles into one view. From there, you can see what the overall trends are among them and supplement your existing customer retention strategies with real-world insight.
Data enrichment - Combat data decay by continuously updating names, phone numbers, and emails. Combine these with specific pieces of unique information about each customer to form a much richer and more accurate picture of your buying audience.
Data quality - Ensuring data quality can be a challenge, as it means determining what your data requirements are upfront, how to create them, and the level of tolerance for errors your organisation will have — a job few people want. But automating data integration eliminates most of the risk of not complying with your company's data governance policies, increasing both the accuracy and the value of the information available to teams across the organisation.
Real-time information delivery - Businesses cannot wait days to crunch numbers; they have hours and sometimes minutes. That's why real-time information delivery becomes crucial for any business to quickly adapt to market, customer, vendor, and even regulatory and compliance changes. Data integration enables you to sample data from any point in the collection process at any time to get minute-by-minute insight into processes, workloads, and interactions.
Invest In A Modern Data Integration Solution
Historically, data integration has often been performed in an ad hoc manner by individuals charged with producing reports based on data from different systems or applications. But when manual processes are used, or even if several generic software tools are cobbled together to complete the task, extracting needed information from disparate streams of data in a timely fashion can be extremely time-consuming, difficult, and error-prone. And as most organisations use a number of systems and applications residing on cloud, social and mobile platforms, integrating this data in an efficient manner becomes even more critical.
The challenge with data integration starts when attempting to merge data from legacy systems into a new system. Due to inconsistencies such as formatting, spelling mistakes, duplicates or naming conventions, it is important to conduct a thorough cleanse, ensuring only reliable data is brought across to your new system. This will ensure a successful data integration project with minimal delays and costs.
Fortunately, automated data integration processes can gather structured, unstructured, or semi-structured data from virtually any disparate source into one place. Consolidating data to a central repository enables teams across the organisation to improve performance measurement, gain deeper insights and actionable intelligence, and make more informed decisions to support organisational objectives.
A well-designed data integration solution, such as SQL Server Integration Services (SSIS) or Microsoft Azure’s Data Factory in the cloud are examples of this. They can automate data integration, and allow the creation of blended datasets without manual coding or tuning. This provides connectivity between a wide variety of data sources. In fact, Azure Data Factory integrates data silos with a service built for all data integration needs and skill levels. Easily constructing ETL and ELT processes code-free within an intuitive visual environment, or write your own code. Visually integrate data sources using more than 85+ natively built and maintenance-free connectors.
Good Data Integration Drives Results
So, if increased efficiency, better access to data and more timely insights aren’t enough of a reason to improve your data integration processes then what about an uplift in profit and customer numbers. Aberdeen’s Data Integration survey goes on to show that companies using data integration outperformed their competitors who didn’t across multiple metrics. These businesses reported a 9% increase in operating profit year-over-year, compared to a 4.6% increase for companies with no data integration. Furthermore, companies with data integration increased their customer base by 8.1% on average, compared to 5.2% for companies with no data integration.
As businesses continue to depend on their data to remain competitive, having a well-designed, automated and modern approach to data integration will become even more critical to deliver a single source of the truth and provide the necessary foundation for a successful analytics framework. Download Gartner's 2019 Magic Quadrant for Data Integration tools report for an understanding of why Microsoft was named a Challenger in this space & why data integration is critical to get right.