Why You Need To Break Data Silos To Build Powerful AI Systems
While new innovation such as cloud, open-source tools, containerisation and automation has helped fuel innovation, it hasn’t impacted business outcomes to a large extent. In fact, the bulk of the analytics solutions and traditional big data platforms have been inconsistent with producing their fundamental promise of scalable AI models. According to analysts, artificial intelligence deeply depends on the quality of data, and if the data exists in silos, it would be detrimental to the future of artificial intelligence across business organisations. But why do data silos exist? Data silos are individual collections of data which are stored and managed for a particular purpose, and for a certain business function. Data silos exist because IT projects are performed or applications are deployed within specific areas of the business without much consideration for the integration of the data or using it in a broader business context. Bringing down data silos is one of the biggest challenges stated by executives, business leaders, and data and analytics professionals alike. In fact, research has found that the large chunk business leaders believe that their organisation is focused on eliminating data silos. Data silos, in turn, make it more complicated for IT professionals and lead to delays for business leaders who need data-driven insights rapidly. Data silos also prevent the productivity of the analytics team, leading to longer analytics cycles, diminishing trust in analytics, and many times preventing the delivery of results. Removing Data Silos Is Critical To Build Scalable Models Cloud has provided limitless resources for organisations to expand their datasets. The expanding storage of data both on the cloud and hybrid scenario has made it difficult for organisations to consolidate and analyse data. As data overload remains scattered across various disconnected silos, both on-prem and across the cloud, it becomes cumbersome to run it through machine learning models. This has led to a scenario where businesses capture a lot of data but put very little data to use, as most of it remains siloed and unstructured. There may also be a situation where IT teams don’t have an idea of where a piece of data is stored because of complex on-premise and cloud data stores. For example, if we look at data science, there is a difference between doing data science at a local scale versus on a cloud-scale. Most of the data science that happens today is either located at your systems or laptops or within a local server. As we move ahead, the whole idea of a scalable machine learning or AI will move from local to the cloud, according to analysts. This means that the components of the pipeline will also change when it comes to different cloud platforms. In fact, big data researchers dislike data silos, they believe they should be removed entirely. From their perspective, the largest hurdle hindering the scale of big data and advanced data analytics isn’t a shortage of skilled workers, but a lack of access to proper data assets. Due to security and compliance issues along with legacy IT systems, large chunks of data remain in silos. Many times, data science professionals build models in a vacuum based on the data they have. Teams need to focus on building data lakes or data warehouse that allow for a single repository of data, contrary to a siloed approach that makes data scattered across different places. Even a large part of machine learning work is done in silos. “Right now, with the kind of pipelines we have, there are many loose components, which are not talking to each other and sitting in silos. But, MLOps is different from the actual data science we do today, and it can facilitate those communications between the different components in the ML pipeline,” says Lavi Nigam, Data Scientist at Gartner. Businesses Must Consolidate Data For AI Innovation The major challenge for businesses going forward is building an IT infrastructure which can tear down data silos by making data integrated and available, and at the same time assuring security and compliance. With the availability of affordable compute and storage, organisations can process more data at lower costs, with regards to data volume and velocity challenges. So, regardless of the challenge, they will have to achieve this to derive value from data and build competitive AI models. Businesses, therefore, need to consolidate data from different sources such as CRM, ERP, social media, IoT, and PoS to feed it to ML systems. Machine learning can cluster similar items together, automatically identifying meaningful relationships through algorithms. But this is easier said than done. From the perspective of business managers, data silos are essential for keeping sensitive data secure from hackers, which is a reasonable argument given many companies may not have an adequate security architecture in place. Instead, they need a single, integrated cloud data platform that can meet the performance and concurrency for all the workloads, such as data integration and secure governed access to all your data, at scale.
While new innovation such as cloud, open-source tools, containerisation and automation has helped fuel innovation, it hasn’t impacted business outcomes to a large extent. In fact, the bulk of the analytics solutions and traditional big data platforms have been inconsistent with producing their fundamental promise of scalable AI models.  According to analysts, artificial intelligence deeply […]
While new innovation such as cloud, open-source tools, containerisation and automation has helped fuel innovation, it hasn't impacted business outcomes to
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Vishal Chawla is a senior tech journalist at Analytics India Magazine and writes about AI, data analytics, cybersecurity, cloud computing, and blockchain. Vishal also hosts AIM's video podcast called Simulated Reality- featuring tech leaders, AI experts, and innovative startups of India.