Business Analytics Trends

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Blog | Jul 8, 2015

Business Analytics Trends


The Industrial Internet Revolution has begun. By 2017, more than 20% of customer-facing analytic deployments will provide product tracking information leveraging the IoT


BUSINESS_ANLYTICS


Introduction:

Most days we are heads down managing popular business analytics platforms from OracleMicrosoftSAPBirstInformatica and QlikView, but we are also keeping an eye on what is happening in the industry and the trends that will shape business analytics in the next few years.


Here are three trends to consider.

Analytics finally embraces the cloud. Cloud-based data warehousing is on the rise.

According to 2015 InformationWeek’s Analytics, Business Intelligence, and Information Management Survey, cloud-based data warehousing services show the biggest increase in adoption of any information-management category, jumping to 34% from just 24% in 2014.

Cloud-based solutions will be critical to helping organizations expand access to data and analytics as well as increase their agility with their data. Taking advantage of the flexibility and cost model of the cloud, these solutions will offer performance on demand without the management overhead and cost of traditional on-premises offerings.

The Industrial Internet Revolution has begun. By 2017, more than 20% of customer-facing analytic deployments will provide product tracking information leveraging the IoT

In this Forbes article, Gartner claims that the Internet of Things (IoT) is on the rise. Fueled by the Nexus of Forces (mobile, social, cloud and information), customers now demand a lot more information from their vendors. The rapid dissemination of the IoT will create a new style of customer-facing analytics — product tracking — where increasingly less expensive sensors will be embedded into all types of products.

We are being pulled into IoT projects by companies that are looking to connect and monitor equipment and machinery. Data warehouses had to be developed to take the data and report on top of it, and information had to be correlated with the product cycle in order to provide better information that could then result in a more efficient way to produce feedback. Once the data warehouse was complete, business analytics could be utilized to analyze the data. Another example of an IoT project involved monitoring snow plows. GPS devices were connected wirelessly to monitor plow location, road conditions, and what chemicals or salts were being deployed and where they were being deployed.

Embedded analytics is the elusive “gold ring”

Embedded analytics is the integration of business intelligence (BI) tools such as reports and dashboards, within business applications to improve the context and availability of information to the user. Industry pundits are claiming that it will be the next and evolution of business intelligence.

There is definitely a trend where many new SaaS-based applications have analytics built right into the application, behind a feature-rich user interface.

In terms of Enterprise BI - where the organization provides a cross functional holistic representation of the organization for reporting and analytics – we see a very muddied and fractured market. Here is why.

Conceptually, embedded analytics largely depends on an organization that has standardized business processes and standardized on an enterprise ERP application that would not require integration from 3rd party legacy applications. For example, companies that have standardized on Oracle eBS are ideal candidates for embedded analytics which extends the application.  As a full solution, the organization has great control over the BI lineage because they have control over the source and can then provide the data model, ETL, BI model and BI content more out of the box.  The time to deploy the BI app is much quicker, and integration of other 3rd party legacy applications is little to none. 

But the reality is that we find the business process standardization landscape is all over the place. It is more common for organizations to have not standardized, which steers them away from preb-built or embedded analytics. Many firms grow through mergers and acquisitions and as a result there are a number of operational and strategic risks that are not identified or addressed such as different business processes and workflows, different products, customers, service of those customers, business systems, etc.  Even if organizations are aware of these challenges they can fall short of addressing them because is too complex or costly.

As a result, most companies have not standardized on single set of ERP/CRM/SCM applications and are dealing with legacy systems. Creating a single EDW representation is challenging when acquiring and consolidating data from multiple ERP/CRM/SCM application since you are constantly managing data acquisition, consolidation, transformation, standardization issues (all before reporting and analytics platform). 

For example, we work with a large equipment manufacture that has 40% ERP data on single Oracle eBS instance, 40% on 18 separate SAP instances, and 18% on 11 legacy instances. They have no plans to standardize.

Cloud-based data warehousing is here today and IoT is coming up fast. We are more cautious about the reality of widespread enterprise-wide embedded analytics.


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