From Data Warehouses to Big Data - Automation Will Kickstart Your Digital Transformation

Why is the automation of both Big Data and traditional Data Warehouses proving so pivotal to Digital Transformation?

Yann Guernion
Yann Guernion, May 12, 2017 12:30 pm
Blog > Data Automation | Big Data | Data Warehouse > From Data Warehouses to Big Data - Automation Will Kickstart Your Digital Transformation

Whatever action you take, the chances are new data will be generated. Go shopping and the point of sale system records your purchase transaction. Walk down the street and a security camera records your movements. Take a holiday and the airline processes your flight, the hotel transmitting monitors your stay… even the sun lounger attendant with his mobile phone is data.

Every digital process and social media exchange produces data. And this Big Data is arriving from multiple sources at an alarming velocity, volume and variety. By 2020, for example, about 1.7 megabytes of new information will be created every second for every human on the planet. Facebook users send on average 31.25 million messages and view 2.77 million videos every minute. And by 2020, at least a third of all data will pass through the cloud.

There’s no denying there’s a lot of data floating around. However, that data is only useful when it’s been processed. In this era of the application economy and business transformation, organizations need to capture, process and analyze data at an exponential rate. That’s not easy though. You have to distribute that Big Data to hundreds of downstream applications – frequently in real-time. You need to be certain the data flows are continuous and scalable, from the source to the analytics. And you need the skills and resources to design and operate the processes supporting Big Data flows.

That issue of data management applies equally to enterprise data warehouse environments, albeit not at the same scale as Big Data. The data warehousing concept puts insight in the hands of decision makers, making your company more agile, innovative and competitive. However, running an enterprise data warehouse requires the coordination of multiple discrete operations across disparate applications, databases and systems – all in the correct sequence, at the correct time and under the correct conditions. Missing one step in the process, or executing a step at the wrong time, can result in a significant amount of wasted processing time and in the worst-case scenario: bad data.

Whether you’re processing data through a data warehouse concept or Big Data technologies, multiple tasks need to be coordinated to ensure it is in the right place at the right time. Timely reporting of data processing must also be monitored to meet service level agreements. And rapid advances in technology mean that end-to-end process orchestration must be easily extensible.

Get that all right and you put real-time actionable insight into the hands of decision makers, increasing agility and competitiveness. Get it wrong and you share bad data or delay decision-making; to the extent your business becomes a market laggard, responding slower than the competition. In other words, you’re toast!

So what’s the answer? How do you orchestrate end-to-end data processing across Big Data or data warehouse environments?

Automation accelerates and streamlines trusted business intelligence

Automation can help integrate all the software used in the data warehouse process into enterprise process automation to ensure the timely delivery of trusted business intelligence reports to your business users. For example, unified reporting and analysis on a single interface enable an enterprise-wide view of all data flows. This allows the business to view its data strategy holistically and makes data accessible to non-technical staff.

At Automic we believe automation should accelerate and control data warehousing processes for timely, accurate, complete, business analytics. You benefit from faster data integration cycles, immediate reporting distribution, faster implementation of new data warehousing processes and compliance across your data flows. Faster data analysis also enables agile operations, meaning IT operations can actually drive agility rather than delaying the realization of business strategy as it often has in the past.

Automation also comes to the rescue of that simple but vital process: file transfer. In most cases, a standard copy procedure can easily result in important files being lost or corrupted, and your IT function is constrained by the number of resources that can deliver file transfers. Automation best practice should integrate file transfer into your enterprise workflows, so there’s no need to maintain separate systems for automating application tasks and data transfer. The result? Faster, more reliable and cost-effective analytics.

Drawing it all together

Every organization needs to quickly and accurately process data of varying volume, velocity, or variety – whether the goal is to analyze buying trends, healthcare patterns or social movements. An automation platform simplifies and accelerates the integration of Big Data and data warehouse projects – from source to staging to reporting. Now is the time to take control of this data processing too. According to analysis, less than 0.5% of all data is currently ever analyzed and used. So just imagine the potential data automation can bring in the future.

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Yann Guernion

Yann Guernion

Yann Guernion is Product Marketing Director at Automic Software. With over 25 years of experience in the world of IT, Yann has a wealth of expertize in managing the entire product line lifecycle.