A smart data infrastructure which enables self-service model, not only solves cost overheads but also ensures effective and timely data dissemination paving way for quicker business growth.

IT HyperConvergence just around the corner – Gartner

With Hyperconvergence happening in the Systems area, Silos will be completely eliminated.Secondly, “One throat to Choke” will be approach taken by all IT infra teams as it is easy to manage security with an converged IT,….in the near future,we are going to see 100s of end points, that does not mean 100s of systems & operations. This is leading to a growing demand in Integrated Systems. Hyperconverged operational & BI systems is the way forward

Microsoft Azure Analysis Services which is an enterprise-grade OLAP engine and BI modeling platform offer a fully managed platform-as-a-service. The success of self-service BI has called into question the viability of technologies such as conventional BI reporting and OLAP.

Most business users don’t have the expertise or desire to do the heavy lifting that is typically required [in the self-service model] to find the right sources of data, consume the raw data and transform it into the right shape, add business logic and metrics, and finally explore the data to derive insights.

The semantic-model-over-raw-data approach allows business users to connect to the model and immediately explore the data and gain insights. Executives can visualize data through business and IT KPIs and make informed decisions from their devices. These business value dashboard can justify IT ops with better representation of IT’s impact and value by bringing business intelligence to IT data

In this article, we compare stodgy enterprise data infrastructures with agile real-time neural structures.

Considering that there are going to get our data inputs from plethora of apps spanning operational systems (ERP, SCM), customer systems (CRM, Channel Data), devices (IoT, wearables) along with AI tags like (Face, Mood, Sentiment); we shall take a look at the infrastructure needed to run the subsequent tasks of learning, analysis, decisions, and actions, post data input.

If we pick the existing data infrastructure, you will see the data processing tasks are spread across different applications. If an enterprise is looking to implement big data solutions, they would typically need to deploy a crowded infrastructure to achieve their goals as shown in the pic below. Organizations typically have multiple goals for big data initiatives, such as enhancing the customer experience, streamlining existing processes, achieving more targeted marketing and reducing costs.

The picture clearly defines a set of tools for processing structured and unstructured data, with structured data heading the data warehouse way and the unstructured getting pooled in the lake, which will be further transformed and assorted.

Post this, data is ready for mining, enrichment, harmonization which is now ready for knowledge discovery, learning, and deducing decisions. These pruned datasets/insights are now ready to be served to data targets. Data targets in the picture reveal systems for business users, but in reality, these insights need to travel back to the systems which act as customer touch-points.

Imagine the kind of time taken due to data hops between systems to deliver insights to any customer touch-point, where the need is to engage customers contextually in real-time.

These systems affect both top-line and bottom line growth of a business, which can be pointed out as follows

Affecting Top-Line Effectiveness

Data Relationship is Incomplete – Due to improper tagging and data redundancy, it is highly impossible to achieve complete datasets

– Due to improper tagging and data redundancy, it is highly impossible to achieve complete datasets Inaccurate Insights – The incomplete datasets, when processed result in inaccurate insights due to data leaks.

– The incomplete datasets, when processed result in inaccurate insights due to data leaks. Latent Delivery – Due to disconnected datasets, extra time is required to bring data together, which is further pushed to another app for analysis or insights, which in turn is pushed to a data target app.

– Due to disconnected datasets, extra time is required to bring data together, which is further pushed to another app for analysis or insights, which in turn is pushed to a data target app. Low Revenue Conversions – Either due to inaccurate insights or post event delivery of insights, conversion campaigns do not make an impact resulting in low effectiveness

Affecting Bottomline Effectiveness

System Overheads – The infrastructure consists of many layers, with each layer requiring apps, which unnecessarily adds up to the total software/systems costs

– The infrastructure consists of many layers, with each layer requiring apps, which unnecessarily adds up to the total software/systems costs Integration Overheads – Due to several apps and their silos, integration tasks are prevalent in this environment

– Due to several apps and their silos, integration tasks are prevalent in this environment Redundant data Overheads– Due to silos and multi-layer aggregation, data is duplicated adding to cost of system resources.

Now, let us look at how real-time data infrastructure can improve effectiveness and help businesses realize increased profitability and sustenance.

The below diagram illustrates the data infrastructure with Plumb5 as the platform for insight delivery.

Plumb5 uses neural distribution concepts to bring in two fundamental differences in order to simplify environment and achieve real-time processing of data.

Plumb5 comes with a data-aware data model where physical data sources and columns need to be mapped. This mapping allows the platform to auto organize data based on the model definitions and is ready for learning and analysis

The propensity scoring engine refers the scores set against data parameters to allocate or distribute scores based on rules to arrive at the net weight. The net weight is matched against the grid containing score range(or a state) with an associated rule to trigger. Based on the match, the trigger fires a template to the designated data targets.

This ensures immediate conversion of incoming data inputs to insights, which can be used to trigger an action instantly.

The data mapping exercise makes sure that the data relationships are holistic ensuring accuracy in datasets. Running learning models over this dataset will contribute to accuracy in insights which when served in real-time, increases effectiveness in communications. Effective customer communication leads to higher top-line growth.

The Bottomline benefits are clearly visible as system/tool overheads and integration overheads are reduced drastically. The data model design makes sure there is zero data redundancy.

Such a design will not only benefit the large enterprise needing a big data solution but can be implemented by any business size, who would like their data organized right from day one. This saves them from unwanted costs that they will spend in the future to bring data together to analyze and deliver insights

In our view, irrespective of the size of the business, every business goals would be to enhance customer experience, streamline processes, achieve targets and to reduce costs. To accomplish their goals, they will need to consider a smarter environment which is agile and effective.