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Here is one of the common questions that I hear from customers:
 
How do we make sure that what we are investing today in the analytics space is applicable and relevant to tomorrow in other words, how can we future proof our analytics architecture?
 
I have outlined five steps to future proof enterprise analytics architecture from capabilities standpoint:
 
The approach that has worked very effectively for us over the years in helping customers navigate across the Analytics Maturity Curve is “Think BIG, Start Small, Deliver Value” (Fig 1.3) as it allows organizations to evolve at their own pace.
 
Data Readiness
Business objective/goal alignment
Data Storage& Security
Data Consumption and
Data Access
 
Each one of these five capabilities is critical and essential not only from analytics architecture relevancy standpoint but also to maximize the value of the investments organizations made in the analytics space while lowering the TCO (total cost of ownership)
 
Let’s look at these five steps closely.
Table 1: Five Steps to Future Proof Data to Decision cycle
 
1. Data Readiness:
The volume of the data and the variety of data in the organizations is growing with great velocity. Managing this data is the foundation block for the success of any analytics / big data related initiatives. Organizations need to find ways to quickly validate, cleanse, harmonize, integrate and govern the data on the fly. It is essential for companies to consider investing in an integrated enterprise information management platform that deliver this promise. Once the data is ready, it important to categorize the data based on its value. Not all data is created equal.

2. Data Value:
Categorizing the data based on the value of the data for business is critical. Data can be categorized based on frequency of access broadly into hot data (frequent access), warm data (medium frequency) and cold data (low frequency). Once the data value is established, the place to store the data by its value need to be set.

3. Data Storage& Security:
Data storage is a key consideration not only based on the data value and costs for the storage technology but also the unique value proposition of the storage technology and it’s very important that these data storage technologies offer multiple levels of data security. Common data storage technologies include: in-memory/real-time/big data technologies, databases and multiple other sources. Consuming the data that is stored becomes the natural next step, after all the purpose of the storage is to retrieve the data on demand as needed.

4. Data Consumption:
Data consumption may be broadly classified into three main categories: self-service analytics where users can self-provision the data they need for any type of analysis while guided analytics on the other hand are provisioned by IT for consumption by business in a pre-defined path. Guided analytics may also include event streaming analysis. Lastly and most important is Predictive Analytics where business users can model the data for ‘what-if’ analysis, likelihood analysis leveraging pre-defined and user-defined advanced mathematical algorithms and techniques. Purpose built analytic applications also fall in this category. Once data is consumable, the access point of data becomes very important.

5. Data Access:
Data Accessneeds to be kept very simple, secure and easy. Business users typically prefer to access data via Portal, Web browser and Mobile devices.

We believe these five capabilities will remain as relevant to tomorrow as they are today.Data is growing and decision cycle times are compressing. Hence any organization that is working on speeding and simplifying thecapabilitiesrequired by an organization to move from data to decisions quicklyacross the enterprise is not only future proofing itself from analytics architecture standpoint but also well positioned to improve organizational performance and the speed of innovation.
 
- By Srini Pagidyala
 
About ProMorphics:
 
 
Innovative Analytics| Transformational Business Value| Committed Management and Dedicated Team
 
ProMorphics provides solutions for enterprise customers that improve organizational innovation and performance through the right use of analytics by Maximizing data value. We achieve this through best business practices and higher-levels of collaboration with customers which earned us the status of “trusted advisor” with our customers who proudly showcase their solutions and share their transformational success stories. We are the most dependable analytics partner in North America using big data and mobileanalytics for customers leveraging SAP HANA, BusinessObjects and Mobility.
 
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