Moving data from source to target with ease

Moving data from source to target with ease

By Carl Butler Qlik Data Integration sales executive, iOCO.

The challenge of moving data from its source in its raw state to its analytics-ready target state has become increasingly difficult. This is due to the exponential growth of data creation and collection in recent years due to digital transformation processes and adoption.

Businesses are eager to extract maximum value from this new data, but data teams are struggling to manage the volume and complexity of the data.

It’s a challenge that demands attention. This is where modern data integration technologies come in. As a result, traditional methods of data integration are no longer sufficient for many organisations.

Back in the day, the process of getting from raw data to analytics-ready data involved several challenges that needed to be addressed before meaningful insights could be derived. These included:

Data collection: This was arduous, albeit we didn’t have to deal with as much data as we do in today’s world. Nevertheless, this still required significant effort and coordination to ensure the data was accurate and complete. This is understandable when one considers that raw data needed to be manually scripted and extracted from various sources, internal databases, or external sources, such as websites or third-party providers. Add to this the fact that even physical documents had to be added.

Data cleansing: Once the raw data was collected, it needed to be cleaned and transformed into a format that could be analysed. This involved removing duplicates, correcting errors and standardising formats, and the adage of “garbage in garbage out” stands today, as this is probably still what keeps data professionals up at night.

Data integration: Data from multiple sources often needed to be integrated to create a single, unified dataset. This required careful mapping, alignment and massaging of data fields to ensure consistency.

Data normalisation: Data needed to be normalised to ensure it was consistent and could be analysed properly, which involved converting data into a standard format and removing any anomalies that would skew the data.

Data storage: Once the data had been cleaned and normalised, it needed to be stored in a format that could be easily accessed and analysed. This required significant storage and processing resources.

Analytics: Finally, the data could be analysed using a variety of statistical techniques to extract meaningful insights. This required expertise in data analysis and the use of specialised tools and software.

Traditional methods of data integration are no longer sufficient for many organisations.

Overall, getting from raw data to analytics-ready data using traditional methods was time-consuming and resource-intensive, and required significant expertise and coordination. However, the insights that could be gained from this methodology were invaluable in driving business decisions and improving performance. This, in turn, drove vendors to keep developing their software to streamline these processes.

Today’s landscape

In the modern data world, however, there are still challenges to getting from raw data to analytics-ready data, but thankfully new technologies have made the process faster, more efficient and more automated.

This ultimately benefits any organisation attempting to build a modern data pipeline, and considering how scarce experienced data resources are these days, any reduction in build time is welcomed.

According to Gartner, adaptive artificial intelligence systems, data sharing and data fabrics are among the trends that data and analytics leaders need to build on to drive new growth, resilience and innovation.

The global research giant goes on to note that these data and analytics trends will allow companies to anticipate change and manage uncertainty. It stresses that investing in those trends that are most relevant to the organisation can help meet the business leadership’s priority of returning to and accelerating growth.

It encourages businesses to consider proactive monitoring, experimentation, followed by aggressively investing in key trends based on their urgency and alignment with strategic business priorities.

Forrester emphasises that data holds the key to improving customer experience and operational efficiency, which in turn fuels company success. Unlocking data’s full potential relies on sound data analysis. This leads nicely into an examination of some of the key modern ways and challenges to get from raw data to analytics data:

Automated data collection in real-time: With the rise of technologies like change data capture, data can be automatically collected from a wide range of sources, delivering real-time data to the organisation without putting any strain on the transactional source systems; however, ensuring data quality and accuracy can still be a challenge.

Agile data warehouse/data lake automation: Business needs are changing fast. Business intelligence or data teams need to rapidly integrate and transform data to meet real-time analytics requirements. Struggling with brittle, hand-coded data warehousing processes is time-consuming, and increases management complexity, cost and dependence on development resources that are difficult to acquire these days due to the skills shortage the whole world is facing but especially South Africa.

All in all, modern data management tools have radically changed the clunky data pipeline of the not-so-distant past from running drawn-out projects due to manual processes, to the next-generation set of tools that employ the low code/no code set of data integration mechanisms.

The latter is rich with automation and ultimately saves organisations time and money without the need to manage large teams. This on its own should pique the interest of data leaders out there to consider modernising their data pipeline.

In my next article, I will elaborate on the nuances of managing data governance and quality, and highlight some of the trends that will help accelerate business agility and time to value from data assets.

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