Companies operating in today’s digital economy seek insights from data for competitive advantage. Data insights improve efficiency, enhance customer satisfaction, and unlock new possibilities. But making sense of data has never been easy. The increasing complexity of the business environment makes data management more challenging.

Today’s data landscape is hybrid and varied. As businesses grow organically, data spread to many systems and databases. Business exigencies lead to shadow IT, which throws up data silos. Several applications and data move to the cloud, while other data remain on-premises. Data volumes have also increased exponentially with the coming of age of IoT. The bulk of the new data is unstructured. The fragmented business ecosystem compounds the data challenge. Today’s businesses outsource several functions and rely on an ecosystem of external partners. A sizable chunk of valuable data resides outside the enterprise.

All these necessitate new data models. Traditional data systems are no longer relevant in the fast-paced business environment that need real-time analytics. 

Data Fabrics

Data paradigms keep on changing with time. Businesses cope by setting up flexible data models. Among the various options, the popularity of data fabrics is growing fast.

Data fabrics apply Machine Learning and automation techniques to extract the best and the most relevant data, as needed. Data fabric platforms handle multiple domains, including cloud, on-premises, hybrid, and external sources. It co-opts relational and non-relational databases, data warehouses, flat files, and more.

The underlying algorithm that powers data fabric platforms:

• Integrate data pipelines, cloud environments, silos, and other data sources.
• Use meta-data to organize disparate data sources into exclusive data schemas.
• Predicts the usability of the data sets in new patterns and enables the orchestration of data to such ends.

Data fabrics offer several advantages over conventional data access methods, such as APIs and data mesh. For one, data fabrics simplify data access in a heterogeneous environment. It brings resiliency into data management. Users get complete control over the data, with end-to-end visibility encoded to the fabric. The deep insights and automated possibilities extend data fabrics to a wide gamut of use-cases. Pre-packaged modules make it easy to establish connections to any data source. Users may map data from different apps to improve real-time decision-making.

The importance of data fabrics will increase in 2022 as enterprises adopt more AI-powered use cases. The latest findings by Big Market research estimate the global data fabric market to be worth $4,546.9 million by 2026, with a CAGR of 23.8%.

Data Lakes

As data volumes increase, traditional systems, even if automated, cannot cope with the increased volumes. Traditional data storage methods do not cater to the immediacy and varied use of enterprise data. For instance, businesses store data in traditional warehouses for specific use cases. Repurposing such data for new use cases is difficult and costly.

Today’s businesses grapple with huge data volumes and seek real-time insights. Even a few seconds of delays have severe consequences for several companies operating in sectors such as healthcare, stock trading, autonomous vehicles, and more. Other sectors such as banking and marketing also seek to increase the use of real-time data analytics. 

Visionary business leaders shun traditional data management moorings and embrace the latest technologies to enable speedy analytics. Indispensable to such a scheme of things are data lakes or central data repositories to store raw data. 

Data lakes are large repositories that hold any data type, including PDF, images, audio, video, and other unstructured data, at a low cost. The schema-less nature of data lakes means the data remains in its native form and keeps its original attributes. It becomes easy to use the data for any use case.

Data lakes are agile and cheaper compared to traditional data warehouses. The ability to store any data ensures resource optimization. Data Lakes decreases storage costs substantially and offers richer customer insights.

Traditional data lakes, such as Hadoop, are time-consuming to set up. But new, robust solutions such as segmented data lakes offer ready-made solutions that execute fast.

Data lakes are always at risk of turning into data swamps with relevant information hidden deep inside tons of useless information. Turnkey data lakes solutions preempt such scenarios. Such solutions come optimized for speed, efficiency, and robust performance. Enterprises can leverage such solutions to unlock scaled analytics and AI insights and deploy them in minutes. Pairing data lakes with customer data platforms combines historical data with real-time data.

MLOps

Embedding decision automation in applications is becoming popular, especially in IoT. Machine Learning embedded devices decide on the spot. They need not wait for human or remote interventions.

However, building and deploying advanced Machine Learning systems face several technical challenges. Data changes continuously. Maintaining the performance standards of the model and ensuring AI governance is hard. Often, communication gaps between technical and business teams cause project failure.

MLOps or Machine Learning Operations combines data engineering, machine learning, and development operations to give a fillip to data management. The overriding objectives of MLOps are to improve collaboration and communication in data science. Applied the right way, these tools simplify data management and automate data models on a large scale. 

MLOps integrates ML systems development (dev) and ML systems deployment (ops). These tools:

• Standardize and streamline the continuous delivery of high-performing models, and resolve data dependency.
• Creates “lifecycle” or procedures. These procedures make explicit the what, why, and how of the data project at each stage. It also creates reproducible models that serve as benchmarks for new projects. 

Robust automation capabilities make MLOps the preferred option for several new-gen data applications. Several logistics and travel companies use MLOps to personalize holiday recommendations. Retailers apply MLOps to detect fraud in order management. Financial services use MLOps powered AI platforms to automate loan approvals. The opportunities are endless.

Technology is always in a state of flux. Businesses that embrace data fabrics, data lakes, and MLOps leverage critical real-time insights. They become well-positioned to serve customer needs exemplarily well. Here are five focus areas of AI-powered Big Data analytics in 2022, which complement the use of data fabrics, data lakes, and MLOps in enterprise settings.