The potential of data to deliver tangible business results is undisputed, whether it is to increase efficiency, identify new revenue streams, or increase employees’ ability to serve customers. However, many companies struggle to get the full picture of their data. Most midsize to large businesses typically have siled data in disparate systems, as well as in a variety of schemas and formats, which can be difficult to unify. This means that analysts often have to rely on outdated or incomplete data, hampering their ability to experiment and innovate.
Many data strategies are now focused on whether or not cloud-based data warehouses and data lakes provide better availability for analytics, machine learning and data science projects. Yet, as new cloud-hosted data sets are more flexible and readily available, the challenge most businesses face is how valuable data from different systems and storage can be integrated into these platforms. cloud-based shapes at the speed the business demands.
This is of course no small task. Hadoop was once hailed by the industry as the solution to bring all different types of data together in an agile environment, however, the complexity of managing this data store in an on-premises collection of open source modules has proven its downfall. Ultimately, CDOs and CIOs know where their valuable data is – it’s in their ERP and CRM systems, for example – but the problem arises as to how they can provide near real-time access to it. transactional data in a format optimized for reading. analytical systems process.
ETL fails to meet business expectations
To overcome this challenge to date, organizations have turned to the Extract, Transform, Load (ETL) process to copy data between different data sources. However, the business requirements for data are more agile than what ETL can actually deliver. Moving transactional data to data warehouses where it can be governed, cleansed, and queried, for example, typically takes six to nine months.
This can contribute to the conflict between business and IT. As consumer expectations have evolved with increasingly intuitive devices built into our homes, like Amazon Alexa or Google Home, we increasingly expect that we can find the information we want, when we want it. This has translated from our experience as consumers into the way we use technology in business.
The IT department’s obligation to provide near real-time access to data has evolved into a business expectation. And that’s understandable – the speed at which ideas are realized has never been greater for business benefit. However, for many CDOs and CIOs, it can feel like they’re caught between a tough and hard place, as traditional processes are unable to enable the agile access to the data and analytics the business needs. It is all too common that a business opportunity has been missed by the time a manual ETL process has been completed.
Accept, it’s time to change
Traditional data integration solutions are proving inadequate for today’s agile business environment. Organizations that want to accelerate the value of their data need to ensure that their data pipeline is able to automatically integrate different data sources in near real time for analysis, whether structured or not.
Change Data Capture (CDC) presents a clear opportunity for organizations to access real-time information, regardless of source or schema. Reading and replicating transactional data from less agile sources through data streaming can help organizations overcome the traditional challenges of building a real-time data pool for analysts. This is where we see the success of combining this new agile data pipeline with cloud-based data lakes and data warehouses.
However, data streaming alone will not provide the agility businesses need. Transactional data in its original form will not be ready for analysis and could cause organizations’ cloud platforms to become a ‘data dump’. True agility cannot be achieved if, once disseminated, another manual process must be engaged to refine this information, prepare it and deliver it before it can be analyzed. Automation is essential.
By automating the tedious and repetitive processes and tasks associated with ingesting, replicating and synchronizing data across the enterprise, data integration software enables organizations to quickly prepare data for analysis. . This means that – often for the first time – analysts have a full, instant and unique version of the truth for their data.
Help your data keep track of your business
The speed at which data can be analyzed is increasingly critical to a company’s competitive advantage – and this has never been truer than during these uncertain times when organizations must continually react to rapidly changing economic and business environment.
Automating the process of streaming data from transactional and legacy sources and refining it for analysis allows businesses to finally have a clear and complete picture to help them scale with business. This will be essential as they move from passive business intelligence to the realization of active intelligence, where near real-time, optimized and up-to-date data gives individuals the knowledge and confidence to respond with agility. that these times demand.
- Ted Orme, Head of Data Integration Strategy for EMEA Qlik.