Techaisle Analyst Insights

Before you attempt any modelling you should first look at the inputs and outputs that you want to go in to your modelling. Here is the matrix:

What you need to do is to make a laundry list of the variables (inputs) that affect the output. Typically in a marketing company one would look at sales as the output and a whole lot of variables as inputs. Let me look at a few examples for these cells.

1.       Measurable-Controllable Variables

GRPs of your brand through TV advertising are measurable and controllable.

2.       Measurable-Not-Controllable

Inflation is measurable but not controllable

 3. Not-measurable – Not Controllable

The amount of investments made by your competition in dealer incentives is neither easy to measure accurately nor can you have any control. But this activity impacts the sales of your brand.

4. Not Measurable-Controllable

Not measurable generally refers to qualitative issues which are quite often measured by a pseudo variable, for example: Quality of your salesperson.

In your business environment if the majority of your input variables are in Cells 1 and 2, and you feel that these make a big impact, then modelling will be successful. If not, and many variables are in Cells 3 and 4, modelling will not be a success.

Most companies do not undertake this simple preliminary exercise of classifying the variables that impact their business and then hit potholes throughout the design testing and implementation.

Unclassified variables are veritable landmines. Watch out for them.

Dr. Cooram Ramacharlu Sridhar (Doc)
Techaisle

Underutilization and the complexity of managing growing data sprawl have spawned several trends during the last several years. Data-as-a-Service (DaaS) is one such trend which represents an opportunity to improve IT efficiency and performance through centralization of resources. DaaS strategies have increased dramatically in the last few years with the maturation of technologies such as data virtualization, data integration, MDM, SOA, BPM and Platform-as-a-service.

Within the corner offices of business heads, data scientists and analysts several questions are being asked:

• How to deliver the right data to the right place at the right time?

 

• How to “virtualize” the data often trapped inside applications?

 

• How to support changing business requirements (analytics, reporting, and performance management) in spite of ever changing data volumes and complexity?

In the early years most of DaaS initiatives were limited to financial services, telecom, and government sectors. However, in the past 24 months, we have seen a significant increase in adoption in the healthcare, insurance, retail, manufacturing, eCommerce, and media/entertainment sectors. This is because of massive amalgamation of extracting continuous insights from structured and unstructured data, liberation of data restricted and protected within silos to the enterprise level and the express desire to conduct real-time analytics.

Businesses are looking to solve tough data and process integration challenges as they once again begin to invest in new business capabilities. Data as a Service (DaaS) is based on the concept that the fragmented transaction, product, customer data can be provided on demand to the user regardless of geographic or organizational separation of provider and consumer. Additionally, the emergence of PaaS and service-oriented architecture (SOA) has rendered the actual platform on which the data resides also irrelevant.

Data as a Service (DaaS) has many use cases:

1. Providing a single version of the truth;

 

1. Integration of data from multiple systems of record

 

1. Enabling real-time business intelligence (BI),

 

1. Federating views across multiple domains;

 

1. Improving security and access;

 

1. Integrating with cloud and partner data and social media;

 

1. Delivering real-time information to mobile apps

Data as a Service (DaaS) brings the notion that data related services can happen in a centralized place – aggregation, quality, cleansing, enriching and offering it to different systems, applications or mobile users, irrespective of where they were. DaaS is a major enabler of the Master Data Management (MDM) concept.

Master Data Management is the Holy Grail in data management.  The focus for most businesses is on the single version of the truth or Golden Source “Product”, “Customer”, “Transaction” and “Supplier” data.  This is because:

• Fragmented inconsistent product data slows time-to-market, creates supply chain inefficiencies, results in weaker than expected market penetration, and drives up the cost of compliance.

 

• Fragmented inconsistent Customer data hides revenue recognition, introduces risk, creates sales inefficiencies, and results in misguided marketing campaigns and lost customer loyalty.

 

• Fragmented and inconsistent Supplier data reduces efficiency; negatively impacts spend control initiatives, and increases the risk of supplier exceptions.

MDM provides the plumbing that enables DaaS solutions. This plumbing allows for:

• Agility & Time to Market – Customers can move quickly due to the consolidation of data access and the fact that they don’t need extensive knowledge of the underlying data. If customers require a slightly different data structure or has location specific requirements, the implementation is easy because the changes are minimal.

 

• Cost-effectiveness – Providers can build a base with data experts and outsource the presentation layer, which makes for very cost-effective report and dashboard user interfaces and makes change requests at the presentation layer much more feasible.

 

• Data quality – Access to the data is controlled via data services, which tends to improve data quality, as there is a single point for updates. Once those services are tested thoroughly, they only need to be regression tested, if they remain unchanged for the next deployment.

 

• Cloud like Efficiency, High availability and Elastic capacity. These benefits derive from the virtualization foundation —one gets efficiency from high utilization of sharing physical servers, availability from clustering across multiple physical servers, and elastic capacity from the ability to dynamically resize clusters and/or migrate live cluster nodes to different physical servers.

We find that there is a common process that is appearing within the mid-market and customer customers focused on enabling and MDM strategy. It is the data logistics chain consisting of data acquisition, data stewardship, data aggregation and data servicing.

There is a sudden and dramatic shift in how data is handled in businesses as they are shifting away from a hierarchical, one-dimensional enterprise data warehouse initiative with fixed data sources to a fragmented network. This phenomenon has caused ripple effects throughout the old data logistics network.  Data-as-a-Service (DaaS) at its core is addressing this problem of fragmentation soundly enabled by MDM.

 

Techaisle's Big Data study of 3,360 businesses shows that mid-market businesses typically started their big data journey in one of four ways. However, the highest success rate (determined by reaching a successful implementation of a big data project within six months of initiation) was achieved when an external consultant or organization was brought in to develop proof of concept, advice on database architecture and ultimately develop the big data analytics solution.

Once a decision was made to embark on a big data deployment project, the mid-market organization tended to quickly align behind the initiative. They did realize that big data was not a typical cloud application deployment where independent department purchases could be made, nor was it infrastructure deployment where only IT could be involved. Big data required a new type of alignment between business heads, namely, Marketing, Finance, IT and a completely new set of players known as data scientists or data analysts.

Study shows that businesses are moving from “whack-a-mole” analytics to “business perspectives” to get newer insights into their operations and better knowledge about their customers as they rethink their marketing strategies because mobility, social media, and other transactional services have increased the number avenues for connections with their customers. There are many different tactical objectives for deploying big data projects but the top among them are sentiment monitoring, generating new revenue streams & improving predictive analytics. And businesses are expecting some clear cut benefits from big data analytics such as increased sales, more efficient operations, improved Customer service.

 

Techaisle’s global mid-market businesses’ Big Data Adoption & Trends study shows that the promise of superior data-driven decision making is motivating 43 percent of global mid-market businesses to either invest in or investigate Big Data technology. Out of these, 18 percent of mid-market businesses are actively investing in big data related projects. The possibilities of analyzing a variety of data sources, producing action-driven business insights is too big to ignore for mid-market businesses.

Big Data requires a certain level of IT sophistication and a history in the linear investment in Information Technology enablers to be successfully. While these factors predispose larger accounts to Big Data, the competitive imperative to understand customers, innovate products and improve operational efficiencies has already started to reach down to the mid-Market, forcing a search for how to leverage primary and secondary data that is generated by the business.

The current and planned investment represents a sizable opportunity considering that the segment is relatively new and requires a certain level of IT sophistication and a history in linear investment in Information Technology enablers to be successful. North America has both the largest market and the highest level of investment in Big Data overall in SMB and mid-market segments. Mid-Market attitude towards Big Data transitions from “Over-Hype” to “Must-Have” technology with the increase in employee size. However, nearly one-fourth of lower mid-market businesses consider big data to be over-hyped and yet 29 percent think that it will be an important part of their business decision making process in the future.

Business intelligence by itself has provided enough business insights, however, mid-market businesses are now looking for extracting business perspectives to drive superior decisions and ultimately achieve superior results.  Extracting business perspectives has become important as they rethink their marketing strategies because mobility, social media, and other transactional services have increased the number avenues for connections with their customers and partners.

In addition to understanding customers, mid-market businesses are also considering big data analytics as an important initiative to help them improve operational efficiencies.

Techaisle’s study shows that there are many different tactical objectives for deploying big data projects but the top among them are sentiment monitoring, generating new revenue streams & improving predictive analytics. It must also be said that businesses have figured out that there is a lot of publicly available data which could also be analyzed to their advantage.

The mid-market businesses actively investing in big data technologies are expecting some clear cut benefits from big data analytics such as increased sales, more efficient operations and improved customer service. These objectives differ slightly by different geographic regions. As the growth rates continue to lag in mature economies, the pressure to increase revenue grows resulting in developing robust analysis and extracting insights from all sales and customer data including transactions.

When specifically asked about preferred deployment choice in terms of on-premise vs. cloud, mid-market businesses are unsure as they are still navigating through their technology options. However, Hadoop dominates as the preferred platform but confusion exists.

In terms of analytics skill-set and long-term vision, the potential of linking structured and unstructured data sources to create new business insights is being considered very useful but at the same time mid-market businesses are not really prepared for it. In fact one-third of mid-market businesses agree that linking structured and unstructured data would be very useful for big data analytics but over 70 percent mention that they have either none or very limited capabilities of analyzing unstructured data. This is where they are turning to external help for guidance.

Needless to say, survey reveals that big data deployment is posing tremendous challenges. Technology confusion, lack of skilled resources and potential unclean data are being considered as the biggest roadblocks for big data project implementations. Big data technology and its far-reaching capabilities are being viewed by mid-market businesses as very complex resulting in very steep learning curves.

In spite of challenges, the study shows that there have been some successes when business units, IT & data analysts exhibit extraordinary alignment. Highest success rates for project implementation and generating new insights have been achieved when IT and data analysts work with external consultants from project inceptions.

Detailed Global Mid-Market Big Data Adoption and Trends report is available for purchase. Details are given here.