CEO’s Guide to Data Integration
All businesses run on data. Major strategies are devised, and resources are invested based on the result received after a regressive assessment of the data collected. Businesses are more complex than ever, which requires essential decisions taken quickly and more accurately than ever before. Decision-makers like CEOs often seek the help of Business Intelligence teams to uncover the best strategies for the business. And what constitutes this business intelligence is the transformation of data into actionable insights through the use of software and other services.
In any organization, data is existing at each level, from ground zero to the top. In such cases, it is very challenging to gather all the information and distill it into actionable intelligence.
Data is a significant asset for any organization, be it a financial enterprise or a technology firm. It is forever growing in size and value. Data is everywhere, whether a bank is holding its customers’ data or a hospital containing its patients’ data. Data is essential for any business or company to grow, and how companies deal with the enormous amounts of data will decide who can get an edge in any industry, be it IT, banking, consumer, tourism, medical, etc. Data helps in making better decisions, solving problems, setting up future strategies, improving processes, understanding consumers, and understanding your performance.
How is Data collected?
Valuable data is scattered everywhere. In such a case, it’s essential to collect information-rich and reliable data, measure, and analyze them for better data-driven decisions. This process is called Data Collection.Companies have various ways of collecting customer data. Few commonly used are:
Asking for it: Like if you want to open a bank account, it asks you to fill forms providing many of the details, including personal information. Another way of collecting data is by conducting customer surveys where direct questions are asked to consumers. It’s more or less like interviews, which helps in getting more accurate data.
Company records, mail, phone, and online surveys are other methods of collecting accurate data.
All of the above are examples of First-Party data where a company is collecting data from the customer directly. There are other ways a company can collect data using second-party and third-party.
Second-Party Data: A company collects data from its audience and then sells it to another company. It is First-party internal data, collected by the company (first) and considered as Second-Party data for the company who buys and uses this data. The selling and buying of the data happen directly between the companies, and there is no involvement of the middle-man. It offers the benefits of First-Party data such as data quality, and precision and buyer has more control over the data than the Third-Party data sources.
Third-Party Data: There are companies whose sole purpose is to collect data from various sources and aggregate in an extensive data set and sell it as a package. When a company buys this kind of data, it is referred to as Third-Party data. Many data companies collect and sell this kind of data, such as Acxiom, Experian, and signpost, etc.
Second-Party data and Third-Party data are used for improved Ad and marketing campaigns, target new audiences and observe and predict customer's behaviors. There are many Data Market Platforms and Data Exchange Platforms from where these data can be sold and bought based on one's needs.
Companies collect data from multiple sources, including their websites, customer surveys, social media, and so on. Then there are internal data and external data. We can classify Data in
- Structured data stored in relational databases.
- Unstructured data like videos, photos, audios, extensive documentation, etc., also commonly known as Big Data.
Organizations need devices to store this massive amount of information, which is called storage devices. At the lowest level, we term them as databases. These storage devices are hosted on servers on multiple geographical locations, and they are backed up to recover in case of any disaster to continue with normal business operations. These servers are secured and stored in multiple data centers all over the world as per company needs.
These data centers are mostly on-premises means the company itself owns the hardware and infrastructure. Large organizations like Banks and financial institutions prefer to keep their data on-premise.
In the last few years, storing data on Cloud has also become very popular among small to medium-sized companies. For them, investing massive money in procuring hardware and managing does not make sense. Thus they opt for cloud storage solutions provided by many big players like Amazon AWS, Google Cloud, Microsoft Azure, and others.
Just having data is not good enough for the organizations. They must also know how to make use of it for their future strategy, like coming up with a new product, or enabling intelligent services for their consumers, or solve the current challenges with which they have to deal. Data is the information, but to transform data into usable information is a complex process. Data needs to be evaluated, utilized, and modified, and then only useful information can be extracted from them. As we know that the different types of data are coming from various sources. In general, these different sets of data hold information in isolation and of not much use from the business point of view. That’s where Data Stitching comes in the picture.
Data Stitching is the process of combining and relating the multiple data sets to bring a clear picture. It helps in gaining deep insights and analyzing the trends like knowing your customer expectations and behavior, and how you are performing when compared to your competitors. All this helps in taking data-driven specific and corrective actions for your organization.
Common Data Integration Platforms:
Data Integration is no longer Extract, Transform, and Load (ETL). It has encompassed migration, Batch and Real-Time integration, validation, data quality, and impact analysis. The requirement is now to connect any source to any target. Data integration is of the utmost importance when an organization or a company wants to have a unified view of its data assets. Different institutions may have different methodologies for data integration based on their requirements, but they all need a platform where this integration can take place. Some of the most popular data integration platforms:
- Hevo Data - Hevo is relatively a novice in the field of data management and gaining full recognition for its automated no-code data pipeline to load data into the warehouse.
- It is trendy among data-driven companies and small & medium businesses.
- It provides pre-built integration to 100+ data sources.
- Supports both- Extract, Transform, and Load (ETL) and Extract, Load, and Transform (ELT).
- Easy to set up in a few minutes.
- Fully automated, and requires zero maintenance.
2. Informatica - has hybrid data integration products that integrate data from multi-cloud, on-premise, and hybrid sources.
- It supports data and application integrations on the Cloud.
- It uses PowerCenter for mission-critical enterprise deployments.
- Quick and easy integration.
- Automated processes and intelligent integration.
- High performance with reliability.
3. Talend - has a single, unified suite for all data integration needs.
- It supports almost all cloud service providers.
- Data governance capabilities and data quality are automated.
- Interactive Talend studio is popular among developers.
- Model-driven native code generators make it faster to perform operations.
- It provides flexible plans.
4. Diyotta- It provides smart AI-enabled data integration pipelines regardless of the source data warehouse or format and can migrate data to any cloud-based repository for analysis and processing.
- Supports on the fly data cleansing and transformation procedures.
- It provides the ability for business analysts to query the information without knowing SQL.
- Has a bulk data migration tool where no manual transformation is required.
- Interactive drag and drop User and SQL Editor interface.
- Easy deployment and configuration with the help of Diyotta Manager.
Microsoft, Oracle, and IBM are other big names that provide data integration platforms and capabilities.
Successful Implementations of Data Integration
Data integration is the first step in converting the data into something meaningful and insightful. Many companies have used and are using data integration to provide their business with new aspects.
AstraZeneca is a global biopharmaceutical company. It concentrates on the discovery, development, and commercialization of drugs for diseases in four areas Oncology, Cardiovascular, Renal and Metabolism, and Respiratory. It has its presence in more than 100 countries.
Challenge: The challenge with AstraZeneca had dispersed data throughout the organization in various sources. They were spending more time discussing the data quality than planning business strategies. The requirement was to get a single source of truth that can be used for monitoring across all divisions and markets and provide all required metrics.
Solution: AstraZeneca built a data lake on Amazon Web Services (AWS), and they selected Talend for AWS connectivity, flexibility, and its licensing model. The other value associated was the ability to scale rapidly without incurring extra costs from AWS or Talend. AstraZeneca utilized the entire Talend cloud suite, which enabled 90 percent of the data to be ready for analysis within three months.
Benefits: Results were shortened planning cycles from 15 days to 3 hours. Saving just one month off of each clinical trial helped save AstraZeneca $1 billion a year.
Rabobank is a Dutch multinational banking and financial institution. It is a global leader in food and agricultural financing and sustainability-oriented banking. It has achieved significant progress by applying enterprise-wide digital transformation strategy which has helped the bank to come closer to its customers.
Challenge: Rabobank had an ambitious business goal to deliver 80 percent of all business-to-consumer banking services using unattended, self-service channels within four years.
The problem was in the data quality. The growing number of digitized services was putting pressure on data quality within Rabobank’s Siebel customer relationship management (CRM) platform. Poor data quality was leading to certain online customer activities to be defaulted to physical intervention causing frustrated customers and cost the bank more money and resources to solve the issue. Also, the data quality previously being addressed was expensive and based on slow manual processes.
Solution: Rabobank has been a long-term Informatica customer, using the data management technology across multiple operating subsidiaries, including a data warehousing project that is estimated to have reduced annual development costs by up to $2.5 million. For this data quality solution, Rabobank compared Informatica with other vendors and found that Informatica had better technology, innovative ideas to address the business need, and proven experience in data management.
Informatica PowerCenter Real-Time increased the business agility and performance with right-time data integration, and Informatica Data Quality helped in cleansing the customer data and ensured that data is trustworthy.
Benefits: It helped banks move closer to the goal of 80% self-service delivery as well as enhanced customer experience by ensuring customers terminate fewer online applications. Also, it increased business agility by validating customer data daily compared with weekly previously. It also helped banks in adhering to Dutch and European Union governance, risk, and compliance goals surrounding data privacy and retention due to the high quality of data.
Cure.fit is one of India’s most significant players in the Health, Fitness, and Wellness sector. It offers digital and offline activities across nutrition, fitness, and mental well-being.
Challenge: The company collects loads of data generated by its app and offline centers. The databases like MongoDB, MySQL, and systems like Google Analytics, CleverTap, Freshdesk, Mixpanel, are also used. But only a few team members were able to access the data, and the rest had to wait to get their questions answered. This problem had put the Data Platform team under the limelight. The majority of the time, the IT team was going in gathering data from multiple sources, transforming data, building data pipelines and moving data to their warehouse, Redshift, and then generating scripts and then emailing this to business teams. This gave very little room for the IT team to do more with Analytics.
Solution: Cure.fit needed a modern tool that was easy to set up and would simplify their data integration problem. Since the majority of data being stored on MongoDB, they were particularly interested in a tool that would simplify the integration with MongoDB. Most of the other products evaluated show glitches, along with data issues. With Hevo onboarded, it took them only about two weeks to set the entire system up. The team developed custom Data Models on Hevo that reflected on Redshift, ensuring that all business users are reaching the single source. Thus, it stopped the reliance between the Data team and the business teams.
Benefits: They can now generate over 100 reports daily, and the IT team performance improved. Today, Data provided by Hevo is helping about 150 business users to track their core metrics. It can then use the freed-up bandwidth to focus on more significant analytics projects, warehouse optimization, and more.
Clearsense provides a scalable data platform as a service for healthcare, which empowers digital transformation and real-time insights into operational, clinical, and financial criteria and measures.
Clearsense is a private cloud data environment (data platform as a service) for healthcare to assist health care organizations in a wide variety of data problems that are persisting. In a healthcare environment, there may be as many as 200 plus applications that are producing data artifacts. Just having them in one place in an aggregated form was their primary goal. The major data challenge was the datatype as they are not only dealing with structured data but with unstructured data and the real-time data, which has its nuances.
Diyotta did not just provide ETL capabilities to Clearsense but also an automated data pipeline tool to manage both real-time and batch data with a unified approach. It not only offers the tool but also helps them to utilize its capabilities to the fullest potential. It helped Clearsense to bring new features and functionalities at a rapid pace.
Diyotta’s Interactive, easy-to-use, extensible, scalable, and futureproof technology-enabled Clearsense to do much more and much quicker. It helped Clearsense to promote and accelerate their data platform as a service for healthcare organizations. With Diyotta’s help, the data pipeline and data processing activities are automated. They used to take hours before and now are getting done in minutes.
Intelligent use of data is the need of the hour. Everyone is quite familiar with this new oil and its importance in the development of the business. The correct compilation and assessment of data help you to improve your business decisions, and increase your ROI (Return of Investment). It also enables you to develop more accurate strategies and improve customer satisfaction. Data integration is a must for you and your business. And no matter how big your business is, resources must be invested in the correct tools for the smarter use of data for the better performance of your organization.