Blog
Erp
04 May,2020
Technology

Empowering Enterprises With Actionable Insights

The world, as we knew, has changed drastically in the past few weeks due to the COVID-19 pandemic. No one could have predicted or anticipated so much disruption to businesses big and small and to the world economy at large. 

It seems now that we need to be prepared, as much as possible, for such ‘black swan’ events. Being prepared for situations like these may as well define the organisation leaders of tomorrow.  In order to be prepared for future disruptions, it is imperative that organizations adapt to a data-driven strategy where data insights optimize the distribution of work-loads accessible remotely, with minimal disruption to business operations.

Any data driven strategy resulting into actionable insights is centered around people. Empowering people with relevant objectives and the right tools, drastically reduces the time taken to incorporate and succeed in any such initiative. 

There are few key tenets to be kept in mind while planning such changes to ensure that the strategy is effective and efficient. Some of these are detailed below.

Adopting a data-friendly model 

To start, consider the organizational model that makes sense for your team. You can opt for a centralized, decentralized or a hybrid approach depending on your needs. In a centralized approach, the data and analytics personnel are put onto a single team. In a decentralized approach, the personnel are put into each business unit.  A hybrid approach essentially merges the aforementioned approaches. 

Here is a look at the pros and cons of each style to help you determine the best fit.

 

Approach

Description

Pros

Cons

Centralized

Data, analytics and people are in a centralized structure

Centralization

Improves data quality

Standardization of processes

Better knowledge management

 

Lack of individual business unit domain knowledge

Unclear lines of communication and reporting relationships

Decentralized

Data, analytics teams are within business units

Tailored operating model

KPI’s definition can be specific to needs

 

Data quality decreases

 

Creation of organizational silos

Process standardization issues

Hybrid

Centralized Data governance, quality and technology

 

Granular approach to handle customized requirements·

Easy to build team of sufficient size & relevant skills

Better knowledge management

May not have same impact in either organizational or business unit level

 

 

 

Embracing the new culture

To fully adapt to the use of data and analytics, teams must create and understand the importance of data. Companies usually do not have the commitment required to integrate it into the culture and values of the organization, and that is more often than not the key reason for failure of such strategic initiatives. 

A few things to keep in mind are -

Organizational alignment
It starts with clearly defining your business objectives. Identify any objections that may arise and ask critical questions that may help to take care of all such objections. Once the goal is clearly defined and objections out of the way - identify and address the critical human and organizational 
issues that will ensure a successful transition.

Change management
To drive sustainable change, define and pursue the potential “quick wins.” Sales is typically a good starting point for analytics and big data; a quick return on investment (ROI) is easier to identify, considering the employees are incentivized to leverage insights as it helps them sell more. Ongoing training leads to proper usage of analytics, and even helps with the development of new workflows as new scenarios emerge and such regular participation also ensures that the data centric mindset change is ingrained on a continuous basis.

Communication
There needs to be a lot of communication during these times and it matters how you do it. First and foremost, it is imperative to make clear the importance and need  of  shifting to a data mindset for the organization and that is not possible without the contribution of everyone. Usually, the move to data and analytics driven culture is kicked off with much fanfare only to be pushed aside by the next company initiative. Needless to say, this would ensure that the data transformation journey will stop even before it starts. Instead, slowly incorporate data and analytics into all company communication and initiatives so that it is seen as part of the culture, and ensures that it stays.

Executive buy-in
Executive sponsorship is of utmost importance for the data-centered organization. As with communication, this isn’t something that can be done on a one-time basis but must be practised regularly with enthusiasm.

Incentivising the Team

The final step and one that is often overlooked in the transformation process are company incentives. Leadership can put incentives into place to motivate people to leverage data and analytics daily which will result in greatly improving the outcome of the transition. 

A few options are: Recognition and/or Performance-based rewards

Recognition is a great way to get team buy-in and ensure employees begin to leverage data to drive business performance. Managers should be trained to assist and recognize when their team is putting data to work within their roles. Even if not done perfectly, recognizing the effort both at the individual level and across the team will familiarize the team with all the ways it can be done and create opportunities to learn in real time. 

Performance-based compensation will help your team stay motivated during the learning and adoption phase. All such rewards should be based on transparent metrics which clearly explain what is required of each person along with measurable key performance indicators (KPIs).


In a nutshell - to truly distill all potential value from data and get actionable insights to fulfill business objectives, leaders must put in place the right organizational model, enable the cultural environment and incentivize well to ensure their people buy into, implement and follow the data analytics initiative.
 

# Enterprises during COVID 19 Pandemic # New Culture of Data Management # Data Management Capabilities

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