A good data management and insight approach should address both structured and unstructured data. As more and more IoT (Internet of Things) devices are brought online; as more and more automation takes root not just using devices but technologies like RPC (Robotic Process Automation) and Chatbots; and as Social Media grows to contribute to buying decisions and reputation bolstering; the volume of data that is being created every day is far greater than it has even been in the past. Each day business operations around the world create petabytes of structured and unstructured data. Thankfully with Cloud technology and storage improvements the cost of storing this data in usable form is far lower than it has ever been.
PCS helps its customers deal with the challenges of Data Management and Insight by designing overarching data architecture and strategies that address the following:
- Data security and privacy are primary concerns. Given all the breaches of data that are constantly in the news, companies need to adopt very strong security practices such as two factor authentication, strong passwords and biometrics. Privacy initiatives such as GPDR and other pending legislation will affect what data may or may not be disclosed even if it is de-identified Traditional Data Management policies are built into software platforms and are cumbersome and expensive to change in a constantly evolving legislative and social environment.
- Traditionally structured data that is transactional in nature and has been stored in data warehouses. This includes historical data and aggregations, statistical summaries and their computational details as well. While this data tends to be in fixed length fixed column format, it is very valuable as companies look to modernize their data warehouses to take advantage of Big Data that can feed their AI and Machine learning initiatives.
- Unstructured data such as that from social media sources such as Google, Facebook, Twitter, Linkedin, Mix etc and media RSS feeds. Natural language processing techniques and smart algorithms can help take unstructured data and mine it for meaningful context and to feed a Big Data repository such as a data lake which may then be used in an AI and Machine learning initiative.
- IoT devices, their monitoring, data output and alerts can contribute very positively to an AI and Machine learning initiative.
- RPC and Chatbots generate both transactional and unstructured data. This too needs to be collected in a Big Data repository.
- Analytics and Machine Learning output. The result from the AI and Lachine Learning initiatives itself should also be captured and used to refine input to the ML models.