Data analytics, Big Data, Data Integration, Harmonized data, Data Intelligence – these are terms one comes across frequently. The world is flooded with data, virtual or otherwise – and the endless insights and analytics that data produces are fascinating. But without a platform that can integrate and standardize data at data collection sources, it is quite meaningless.
From a bird’s eye view, data may look like a rich pool, but on closer inspection, this very pool is a collection of unorganized, unstructured data that are of different formats. There are also passive data sets, which could be of potential use, but need a different segregation method to be put to effective analysis. A single platform to manage customer data, beginning with an inventory check of existing marketing avenues, vendors, and customer data collected, is the essence of a good data platform offering.
Is data really an asset?
When organizations are flush with data coming in from so many sources, the more important question becomes how they put this data to use so that they derive maximum value from it? Dealing with a wide variety of data formats, metadata, storage is not easy – challenges of data strategies, cost of data maintenance, the big decision of what data to keep and what to discard are all very valid.
Unless we have usable data, that adds a definitive value proposition, and helps position the company in line with its business goals, data becomes just another liability bogging the enterprise down by the second.
A well-crafted data governance strategy in place from the beginning should be a fundamental practice for any big data project – it helps ensure practices are consistent, common, and responsible.
Need for a data platform
A data platform helps assess, manage, and offer a comprehensive data management facility satisfying the fundamental business needs of the enterprise. The data platform understands the value at a molecular level, derives, and delivers value in the world of big data measured by 4Vs – veracity, volume, variety, and velocity.
That said, most data platforms available manage data of singular nature – say premise data, virtualization data, or application data.
An efficient data management platform works along with a well-crafted data governance strategy, acts as a business driver. Any organization that works will Big Data will readily subscribe to how consistent, clean data can contribute to processes.
Some best practices to build a data platform will set a path for a robust, comprehensive data management platform.
Focus – what, why, how
The key is staying objective, and sticking to the basic what, which, how of the data looking at it from the business goals perspective. What the enterprise plans to do with data, which data is relevant and which data has to go, and how it plans to structure the data. Many companies have data that they cannot find a use for, and continue to store it – storing too much data is a constraint on many fronts. If a clear “what” is defined, it helps immensely in keeping only the relevant data and purging the rest – no matter how pretty it is. Data management software mustn’t get overcrowded or unorganized.
Emphasis – data governance, data protection, and security
While Data governance and data management are different, they share quite a symbiotic relationship. It is an essential step to focus on data governance, protection, and security simultaneously, to ensure the enterprise does not encounter a data breach situation or data of its customer base compromised in any way. Customers may not take it lightly when unknown sources access their data, and in today’s data-world, the most invasive and private data lies in the lake. Security always has to be a top priority in data management.
Lastly, it helps to stay prepared. Having a plan in place in case of a data breach is prudent.
Spotlight – Data Quality
While ensuring only relevant data is kept, and the leftovers discarded is a great idea to improve data quality, it is not the only step. Quality is a continuous process and should be monitored regularly for accuracy, relevance, consistency, and validity. If data that is inconsistent or invalid stays, it may impact analytics negatively. It is, therefore, a good practice to check for clean data before using it in analytics or in generating metrics. Making data quality, in tune with data security, an area of no compromise, helps in building a reliable data platform.
Data Trimming – reduce redundancy:
Remember, the data is very noisy. When data flows in through multiple sources, the chances of the enterprise receiving duplicate data is very high. Having processes in place to reduce data redundancy should be put in place. Putting checks and balances in place to trim data and minimize all data redundancies also results in clean data, which in turn improves quality.
Don’t ignore data backup:
It is efficient to keep everything error free but smart to have a data backup, especially when it comes to data. Accidents can happen anytime, and losing that goldmine of data could be an irreparable loss if there are no chances of its retrieval. Exporting data regularly to a cloud service, or even hard drives make absolute sense. Restoring normalcy after a mishap should be an easy task, and for that to happen – give data back up the attention it deserves.
Assess – Data Readiness:
With AI, ML, and deep learning taking over every sphere, the data platform must be ready to accommodate and host AI, ML technologies. AI and ML have an obvious advantage, improving customer experience and hyper personalizing it. It helps to have a close evaluation for readiness, so the enterprise does not have to struggle during incorporations.
Sharing meta-data through a common metadata layer helps maintain consistency through repetitive data preparation processes. It promotes collaboration, provides lineage information on the data preparation process, and makes it easier to deploy models.
As mentioned earlier, data in itself is very noisy and will not produce any value by itself. There are a lot of unknowns, and having a good data platform gives the enterprise leverage over assets that it can possibly create from its data.
You need a partner you can trust for all your data management needs. To know how YASH technologies is pioneering in this field follow https://www.yash.com/digital-transformation/analytics/data-management/
1.) Doan A, Naughton J F, Baid A, Chai X, Chen F, Chen T, Chu E, DeRose P, Gao B J, Gokhale C, Huang J, Shen W, Vuong B Q. The case for a structured approach to managing unstructured data. In: Proceedings of the 4th Biennial Conference on Innovative Data Systems Research. 2009 Google Scholar
Senior Solution Architect, Big data , Cloud @ YASH Technologies