Data and analytics are central to Digital transformation, but many organizations have not been Effective in it. Additionally, for most, data warehousing, enterprise resource planning (ERP) implementations and business structure have frequently failed to deliver and have left a sour aftertaste.
For these reasons, some organizations are wary of taking on enterprise-level analytics and data efforts. But getting it right can provide phenomenal outcomes, and so, it is critically important to concentrate on data and analytics. We could get it right by avoiding some of the typical pitfalls.
Here are the critical reasons why those initiatives fail and the way to guarantee these issues do not happen in your organizations:
1. Dependency On Technology Deployment As the Main Solution
Sometimes, there is a propensity to hit the simple button and offload the issue by relying entirely on a technology implementation. Technology sellers also push their wares as the upcoming big thing after sliced bread and the panacea to organizational issues. When there's friction between IT and business groups, such conclusions may seem to be the quick response. Often, the knee-jerk response would be to move towards technologies that facilitate a single source of fact and a single source of data. These might be needed but nevertheless would merely be a part of a larger solution.
We need to bring the perfect mixture of people, processes and technology for the data ecosystem in position and the desired future destination. Technology is just part of their solution.
2. Imprecise Current State Assessment
Assessing where we are can assist in properly identifying gaps and opportunities to successfully prioritize and address them appropriately. For instance, each department in an organization has various levels of capabilities and the measurement scales will be different. What's deemed as an ideal environment for one area will be viewed as badly lacking by means of another. Likewise, each and every subject of evaluation (science, utilization, and usage, skills, etc.) should be evaluated appropriately using distinct barometers and rationalized at the enterprise level.
Once the present state isn't understood correctly, the fundamental approaches will probably be incorrect. The focus might be on areas which won't offer considerable value-add. These days, we do not have the luxury of long-drawn jobs. Value ought to be shown. Inability to show consistent progress and results in the get-go causes stakeholders to eliminate trust and funding is sabotaged. The program starts to falter.
3. Employ The Software Development Paradigm Into Data And Analytics Drive
Over time, our culture has become adept at building applications. We think of alternatives in terms of applications development, even when it pertains to data. This means we expect to get a set of requirements and we build a strategy to serve the requirement. The data domain is significantly distinct. Any information endeavor begins with searching for answers to a set of queries. When these are answered, there's a different group of queries. As we learn of new truths, added data sets have been shown and the problem space keeps growing in breadth and depth.
Data and analytics, in an enterprise level, are focused on making the mechanisms to enable business teams to spot opportunities to perform better -- than the attempt is not to create the outcome but to allow it. This requires a different sort of thought process.
4. Wrong Value Proposition Of New Technologies
There are enormous hype and buzz surrounding data science, machine learning (ML) and artificial intelligence (AI). Alpha Go, Alexa, Jeopardy, etc., have elevated expectations into a fever pitch. There is a good deal of potential but not too many organizations have leveraged them. And the issues and domain names where they delivered substantial financial value are a few. In several cases, solving key topics might involve leveraging proven technology. In other instances, the building blocks necessary for innovative analytics are missing.
We ought to carefully evaluate the problems we need to solve and deploy the right techniques appropriately. Determined by newfangled technology to solve basic and fundamental issues is guaranteed to worsen the situation. Some ML issues which have to be solved need a huge number of information, big teams, and lengthy timelines to provide tangible value. Voice recognition platforms like Amazon Alexa is a good illustration.
Access to inexpensive and accessible personal computer power, enormous quantities of data, innovative techniques and the open source communities also have driven the rapid development of this domain name. However, you would like to be careful of when and that problems to use this advanced technology to.
5. Folks And Skillsets
Deficiency of needed skills at the ideal levels, lack of coordination with the staff and misalignment between IT and business would be the most frequent contributors to analytics and data failures.
A variety of skills is necessary for such an attempt, and constructing a well-functioning team is no trivial task! This recognition is the very first requirement for effective execution. In any given team, a few people produce outsized outcomes. For data and analytics success, the initial step would be to begin accumulating a significant mass of high-caliber talent across the respective abilities, functionalities, and domains.
Deficiency of working and mismatched expectations between business and IT should be actively addressed each step along the way. With the ideal amount of authority given to the team and the proper organizational structure, the majority of these become more easy to address.
Data and analytics at the business level isn't a little endeavor. It's fraught with risks and challenges. Knowing these five common pitfalls and fixing them appropriately will considerably increase the likelihood of sustained success.