Article

Vincent Manlapaz
Vincent Manlapaz 25 October 2017

Overcoming persistent in-house "data challenges"

According to a recent study, marketers are often frustrated with the state of their house data, however, their sentiments towards data providers are much more positive. 56% of the marketers surveyed say they’re “very satisfied”, and another 38% are “somewhat satisfied”.

According to Quentin Gallivan (CEO of the Pentaho Corporation), in our hyper-competitive global economy, companies are urgently seeking to harness the value of their data to find new revenue streams, operate more efficiently, deliver outstanding service and minimize risk.

This resonates a compelling argument to scale up data insight and systematic search. In order to actually put any of your hard-won data into practice, you need to have a process that supports its use. The significance of having a clean CRM and the quality of the data collected must be suitable and aligned with business objectives.

Looking at this trend, everyone is prospective to see how others are overcoming in-house data challenges. Openrise (a Data Orchestration Platform), surveyed 175 U.S.-based B2B marketing professionals, representing a diverse group of industries, from companies with over 200 employees to gauge the varying degrees of this issue.

According to the study, marketers are often frustrated with the state of their house data, however, their sentiments towards data providers are much more positive.

 56% of the marketers surveyed say they’re “very satisfied”, and another 38% are “somewhat satisfied”. 

Commenting on these findings, Allen Pogorzelski, VP of Marketing at Openprise, said, “It makes sense that most companies are satisfied with the quality of the data they’re seeing from reputable data providers. The data issues most companies are struggling with are in normalizing and integrating that data from different vendors into their house databases to make it useful.”

Having said, most businesses quickly learned that in order to integrate the data from their house databases, they needed to implement a more robust and sophisticated method not just for data comparison and identification but for consolidation and mapping out of those data.

Arguably, there are a lot of issues to fix and it takes a lot more time especially when you’re working with historical data. However, to effectively understand the new data you’re going to work with, a demand for vigorous data governance and comprehensive data breakdown must be reinforced. This complexity demands data teams spend more time on DevOps provisioning infrastructure than on the data itself.

Integrations with data providers aren’t too bad. According to Databricks,

"The key ingredient to any analytics project is data. However, data comes in many formats and is often stored in siloed data sources such as data warehouses, relational databases — making it difficult to see the entire picture.

Further complicating matters is the need to support large volumes of high-velocity data without slowing down your ability to deliver insights. Organizations often struggle with feeding their analytic models with Big Data as the data and analytics live in different places"

The staggering solution requires significant time and commitment with the higher focus on the actual problems they want to solve.

3 D's to regard:

1) Define the strategy and its process.

Due to the rampant tools being created year after year, most people in the roles considered that software/program is the answer to the underlying issue. The truth, however, it doesn't resolve the problem. People do. To ensures success, business objectives must be defined and laid out open. It is important to note that that chosen software/program should and must be a part of a strategy and the process involved must be implemented.

2) Diverse success through outcomes

There are many roads to success. The prejudice falls on how one is able to get through. Needless to say, business objectives/goals are pointless if they don't achieve the intended organizational change. Business requirements evolve quickly and progress is getting complicated along the way. The unfortunate thought lies in the choice of the solution. One must realize that no solution is silo and fit to all one size pattern. Identifying the patterns and its relationship toward intended outcomes are the primary cornerstone of this reformation.

3) Delivery option for various infrastructure

No purpose defeats its own end goal. Practice and discipline keep the ball moving. To equate the result to the desired outcome, the need to stitch together various technologies platform requires substantial resources to manage and maintain its operation. It was observed then that the lack of a various option for infrastructure slows innovation.

The man behind the successful car which named after him (Ford) made it sense through his own word that most people spend more time and energy going around problems than in trying to solve them.

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