In past blogs, we have focused on the key factors which influence and drive deep, real time insights, speed of insights and insights on all data assets. There were multiple aspects to each factor which were discussed. One common element across all things analytics and by whatever definition we understand analytics is Data. The core component is data management.
“Garbage in, garbage out” is the common phrase used to describe the consequences of bad data and bad data management. This statement still holds true in general. But there is more to that statement. It is not just the accuracy of the data coming in which is critical. That’s brass tracks, but the consistency and accuracy of data definitions and relationships as well as the validity of data combination / mashup are even more critical in this era of information explosion.
Data Accuracy & Data Management
Data cleaning, de-duplication, etc. are standard process which ensure your transactional data is accurate and consistent. It ensures you have a true view of your business performance and your day-to-day operations run efficiently. Consistency in data definitions ensure everyone is looking at the data consistently within the organization and decisions are made using the information accurately. This holds true to all data that are brought together as well. When more types of data are brought together to gain deeper insights and get closer to consumers to be able to preempt their behavior or the market, this not only gets more complex, but adds another layer of challenge in the relationship or correlation of data. Just because I wake up on the right side of my bed and see a market uptick does not necessarily mean that on days I wake up on the left side will cause the markets to go down. The validity of data relationships is as critical.
Organizations typically focus on data by projects. Bringing disparate data together or bringing different groups on to a common platform or system etc. Which is a valid approach, however, what should not be left to lag behind is the data management aspect so that the data does not lose its quality and accuracy from the day after the cleansing. The process and ownership/stewardship should be built along with the projected approach.
The best technology and analytics capability is only as impactful and valuable as the accuracy, consistency and quality of the data, meaning the data management.
Is There a Solution?
Is it possible to have a data warehouse that can speed the consumption of traditional transactional data sources, as well as be able to load real time, streaming and unstructured data without grinding to a halt?
Can you have a data platform that is able to scale and leverage modern, high power (yet still commodity) hardware to accelerate the preparation of the data so that it can be made available for querying as soon as possible?
Is there a way to evolve your data warehouse in order to adopt new innovative technologies that have become available in recent times, to support new business workloads such as Predictive and Advanced Analytics?
Would it be possible to replace your existing aging, and frankly struggling data warehouse, without facing prohibitive costs or high barriers to accessing new technical skills? The answer is, yes (of course).
The Modern Analytics Platform
The Modern Analytics Platform delivers on all the requirements for a next generation data warehouse. Enabling organizations to radically simplify their existing legacy or overly complex solutions in order to lower running costs, improve agility and gain breakthrough performance to deliver real business value. Speed is a key factor in the success of any analytics initiative, and a Modern Analytics Platform must be fast.
Remember to follow the rest of the posts in this blog series where we will explore the answer in our final blog. To find out how the Modern Analytics Platform addresses the challenges of data management, watch the video, The Modern Analytics Platform, to learn more!