For more than four years the current IT landscape has revolved around SMAC technology stack (Social, Mobility, Analytics, and Cloud). However, it is important to understand that the adoptability of organizations towards these trends will vary. While startups and service related companies will be the early adopters of this technology, the core manufacturing companies are still in a wait and watch mode. We should also look at the SMAC stack in the current context. Let us examine these technologies one at a time.

Social – For long many companies have had significant presence in the social media (Facebook, YouTube, Twitter). They have used social media to promote products and services, advertisement and online sales. Big data has been used in a big way to do sentiment analysis and take corrective action. On the social front, even the core manufacturing companies have a significant presence. Social has also contributed in crowd sourcing for new product developments.

Mobile – The adoption of mobile apps in an organization has varied. Currently with the app development market becoming huge, there has been a consumerization of IT services. Users can download and install apps from a store into their mobile phone and start consuming, completely bypassing internal IT team. The challenge comes when IT is asked to provide support for those apps. Hence it is important for organizations to have a mobile strategy in place. It should also state which apps will be supported. There has to be a roadmap for implementation of Mobile applications. Specially applications like Sales Force Automation, Visual Analytics, Expense & Travel management, Approvals has already gone mobile in many organizations.

Analytics – Analytics is key in the decision making process of the connected enterprise of today. Analytics has been adopted in various forms in almost all companies. While in some companies it is still the traditional MIS reporting tool, in most cases there is a comprehensive data warehouse with facts and dimensions. The trend nowadays is to use a visual analytics tool over the data warehouse. This creates a very powerful presentation layer with storytelling capabilities. Powered with predictive analytics the user has the power to look into the future. Data science has also created very powerful models to look ahead. Some of the models used are stated below

• K-nearest neighbor – This method is used to find the nearest neighbors of K where K is a variable and classify the new entrant as positive or negative prospect. This is used in predicting attrition, marketing campaigns and understanding buying patterns

• Moving averages – This method is used to predict future trends. Used in Sales and Revenue analysis

• Affinity – Affinity analysis is used to co-pack products and services. If a has an affinity with b, it means that people who bought a also bought b and therefore it will increase sales if a and b are packed together

• Clustering – This method is used to group similar things together. Mainly used in market analysis to create segmentation

• Decision Tree – It is a predictive model that can be viewed as a tree. Each branch of the tree is a classification and the leaves of the tree are partitions of the dataset. Used in market analytics. Predictive modeling for profit and RoI calculations

• Neural Network – This is an algorithm that can detect patterns, make predictions and learn from large historical databases. This is helpful in doing market analysis with large volume of data before conceptualizing a product. It is also used in fraud prediction

Cloud – In the beginning of this millennium we had services like co-locating and co-hosting. In such a scenario, an organization will co-locate their servers into a data center service provider premises or host an application in the service provider’s server. A few years later this came to be known as Cloud offering with some additional services built into it. While the co-location still exists, the co-hosting is now called Infrastructure as a service (IaaS). We now have Platform as a service (PaaS). Here you can use the cloud service provider’s platform to build your own web applications for quick deployment. These two services have been quite popular and many companies have opted for these services. However, the core manufacturing companies have chosen to stay away. The key reason being the connectivity issue. Cloud relies heavily on seamless connectivity. In India, it is still a challenge in remote areas where most factories or project sites are located. The other bit added to the cloud is Software as a service (SaaS). This is popular for software testing and applications like sales force automation and expense reporting. However, since most cloud service providers don’t have a proper metering tool, you are not sure how much you have consumed. Therefore, you may end up paying more. Security is never a concern with cloud. They are more secure than our organization. The cloud service providers have no choice, but to be secure, since their revenue depends on it.