Businesses can’t extract value from the data they generate because of the prevalence of data and analytics silos. They struggle with limited data visibility and insufficient insights.  As data grows and analytics workloads increase accordingly, infrastructure cannot scale appropriately and storage requirements often outpace compute needs, so the ability to scale these resources independently is crucial to data center efficiency.

Private clouds can address many of these challenges by allowing different analytic applications to work together at their source while maintaining strict enterprise security, governance, and control across all environments.

Obtaining key insights from incoming and existing data has always been a challenge for organizations. Only a fraction of the data that is received is stored, and even less is used for generating value. That leaves a huge amount of unused data that can’t be used in say fraud detection or improving customer experience.

Data pipelines can ingest and store more of this data and help turn them into insights. However, they can be complex to build as they interact with many applications and most parts of the infrastructure in the data center.

Many enterprises would like to make more data-driven decisions, but their analytics solutions are not ideal, whether they are not fast enough, work with poor quality data or don’t use the right models. This means that they can’t understand their customer better which is the biggest challenge most enterprises face.

An approach to dealing with these challenges is adopting more predictive analytics and machine learning solutions. This means better recommendation engines, customer analytics, risk management, and segmentation. They help organizations stay competitive by identifying opportunities and embracing digital transformation.