Leveraging Data Analytics to improve Decision Making, Business Performance, and Profitability

Aurion Data Analytics, Aurion iLeanatics, Aurion Data analysis

Not everything that can be counted counts, and not everything that counts can be counted.

Albert Einstein, Physicist

Every business wants to be data-driven, however, in reality, the majority of them are not able to harness the true power of data. Most businesses today face either of the two scenarios – they have a huge pool of data but do not know what and how to analyze, or they have a business problem at hand but do not know what type of data would be the right fit to crack that problem at hand. This has created a huge gap between the expectations of the business and the reality which needs to be addressed.

In order to bridge this gap, and leverage data analytics to improve decision making, business performance, and profitability businesses should first try to assess and address the three major data analytics challenges that they are facing today :

  1.  Converting Data to Information – The overabundance of data often makes it an overwhelming task for analysts to find meaningful patterns by means of traditional research methods. Modern Data Analytics tools simplify the process of collection and analysis of the huge amount of user data and transform this data into useful information. This information is then used to draw useful insights into consumer behaviour and simplify the decision-making process for business users. However, this is easier said than done.
  2. Data Literacy – Data literacy is the ability to understand, create, collect, analyze, and communicate data effectively in the form of information. The inability of users to understand and work with data makes it difficult for them to ask the right questions which ultimately impedes the progress of implementation of data analytics strategy of any business. To tackle these challenges businesses have to focus on empowering individuals with tools and techniques to make them ready for handling data problems of any form, volume, or frequency.
  3. Changing the behaviour of stakeholders – This is where most businesses fail. Even after spending millions of dollars and setting up the right infrastructure in place, stiff resistance from end-users often makes all the efforts of the management go in vain. Though the new methods might yield a superior performance than the tried and tested methods that are already in use, the innate nature of the users prompts them to resist the change. Proper training and involvement of all stakeholders in the implementation process ease the transition for the users.

Stepwise process of building an effective Data Analytics Solution

In order to tackle all the data analytics challenges effectively and deliver expected results consistently over a long period of time, businesses need to have a data analytics process in place. This process should combine the four key aspects of business namely strategy, implementation, feedback, and improvement. The key steps involved in this process are listed below:

  1. Selecting the right tool – The process of building a data analytics solution begins by selecting the right tool. Below are the points to consider while evaluating the tools:
    • Reliability – The tool under consideration should have a proven track record of consistent performance over a long period of time.
    • Cost – The three major costs that are to be considered are: Development Costs this cost includes the cost of consultants, the time required for development, and the relative difficulty level of development. Maintenance Costs how responsive is the support team, the rate of critical issues with the tool infrastructure and the time taken to resolve them, and the volume of support resources available in the form of documentation and training videos. Licensing Costs – One-time setup and the annual licensing costs of the tool.
    • Ease of use – The end-users should be able to understand and use the applications with minimal training.
    • Scalability – The tool should able to handle the increase in the volume of data and should be flexible to any other changes that the business might need. Ease of customization will be an added advantage.
    • Vision for the future – The tool should accommodate rapid technological advancements. From on-premise tools a decade ago, almost all the applications today are run on the cloud. In the future, there might be other advancements related to Artificial Intelligence directly embedded into the tool. The tool should have a clear development path to include all such advancements.

Below are the top 5 Business Intelligence tools in the market:

  1. Understanding the Big Picture – Once the tool has been selected, the immediate next step is to get to the crux of the problem. The expectations of businesses should match the understanding of the solution provider or consultant. Understanding the big picture helps in avoiding the common pitfalls and prevents misdirection. It helps to drive the efforts of all the individuals involved in the development towards a common goal and keeps them on the right track.
  2. Collection of data – In this step, based on the problem statement all the relevant data from multiple sources is accumulated into a data pool. Any data that does not add value for providing the solution to the problem at hand is not considered for analysis.
  3. Transforming Data to Information – The data collected is then cleaned for any irregularities, outliers, and missing values. Then the data is wrangled to convert it into useful information. After this step, ideally, the data transformed into information will be ready to be used as an input to the tool selected in the first step.
  4. Delivering a Minimum Viable Product – In this step a fully functional pilot application with real data as input is developed and delivered to the users. However, this is not the final product. A Minimum Viable Product mimics all the features of the final application, however, the application development is not frozen. During this stage, feedback is taken from the users after the initial use and all the proposed changes are fed back into development to improve the performance.
  5. Scaling the solution – No business intelligence application is expected to work on limited data sets. There is always a constant stream of data that will be generated over time and the BI application should be scaled accordingly to handle such data.
  6. Training and Development – In this step all the business users are trained on standard operating procedures and key application features. Once training is done application is released for business users.
  7. Support and Maintenance – This step involves in extending support to the users and troubleshooting any minor and major issues that might occur while working with the application.

Key points to consider

Developing any business intelligence application involves multiple teams, the right infrastructure, and associated costs. In order to gain the maximum output, businesses should consider few critical points, which if neglected might prove to be detrimental to the whole process. The key points to consider are:

  • Build mutual trust – To develop an effective business analytics application, the consultants and business teams must coordinate well with one another. In order to build mutual trust, it is advisable to have a clear set of goals mapped against the respective timelines prior to development. Also, before committing to the development of a full-fledged application, developing a pilot application helps to build rapport between the teams and sets the right expectations for future developments.
  • Track the progress – Most businesses make a mistake taking of laissez-faire approach to business intelligence application development. This approach may result in miscommunication and disorientation of the final application. Though micro-management is not a suggested approach, it is advisable to track the progress of the development at crucial stages identified and agreed upon by both parties in the pre-development phase.
  • Do not compromise on data integrity – This is a common problem in data analytics solutions. For the applications to work effectively and perform with consistency and accuracy, the master data should be the same irrespective of the application by which it is consumed. To maintain this consistency and accuracy, it advisable for businesses to maintain a single true source of data.
  • Keep the costs low – This does not mean businesses have to always pick the lowest priced vendor available in the market.To control the costs and get the best output,buddingbusinesses must first allocate a budget for the proposed data analytics solution and then choose the best available vendor within that budget. This restricts the businesses from keeping their costs in control yet achieve the expected results.  For existing businesses with deep pockets, it is advisable to hire a reputed consultant with a proven track record in the market.
  • Drive the change from the bottom-up – The end users are ultimate deciders of the success of any data analytics application. Instead of pushing the change from top-down, it is always advisable to drive the change from the bottom-up. Involving the end-users in the process of application development eases the adoption of the proposed change. Constant training, usage of the application, and feedback are essential for the success of any new change. Instead of directly pushing users to adopt the change, giving them time to get acquainted with the process and educating them about the relative advantages over existing methods improves the adoption rate of the new application.

How to Leverage Data Analytics to transform your Business?

two-dimensional strategy is proposed by Aurion Systems which is proactive (the fact that unknown risks are addressed during initial stages), predictive (separating good vs bad information). These two strategies combined are supported by a solid foundation of Lean, Design Thinking, Analytics, and Digital tools & technology,  where we cut down the efforts and supplement up the human efficiency using powerful assistive tools designed and developed to proactively tasked to do all the heavy lifting (repetitive steps) at a very faster speed without a need to put any human intervention (you are left with plenty of time to think about your future customers at a beach). This helps create success for the Overall Strategy of your Business by utilizing data that matters the most.

Those friends who have come across me just now and interested to know about what I mean by proactive strategies please follow this link. Regardless of the profession/ business, you are in, I am sure we have something to share with each other.

Optimizing the Digital Marketing Strategy for Business

How to start and not stop at the end of this article piece?

If you can’t explain it simply, you don’t understand it well enough.

Albert Einstein, Physicist

I would recommend starting simply by just taking this approach, which has negligible risk, simple and yet an amazingly effective positive step towards our goal of a proactive strategy

A) Take pen -paper or manual method (start now)

Start finding out important questions and problems of your Business, Products, and events (I know this causes efforts, but this will pave the way for better Customer Engagement and interaction), collate the data in Excel-like tools, and try to derive meaningful information using the intuitive charts and graphs provided by these tools. Once the meaningful information starts flowing validate it with the key stakeholders and establish the data models. Gradually, move to Step B when the data volume goes up.

B) Take help from technology

Work towards making it unattended, assisted by using Super-fast digital solution i.e. iLeanatics such that it works autonomously without losing its efficacy by engaging a solid, affordable solution partner.

If you are a CEO/COO/CIO/Managing Director/General Manager who is spending more time in reactive/preventive mode than future-facing, please reach out for an exploratory conversation.

My Contact details

Pradeep Mishra (Director and Co-founder)


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