How to Improve Data Analytics at Your Company

Data analytics techniques are valuable for business managers to derive meaning from the large volume of data generated at various points in all business operations. The trends and insights that data analytics provide help make informed decisions — both strategic and tactical. 

In many organizations today, the challenge is no longer the lack of data, but rather an overwhelming volume to sift through. Analysts are faced with the task of identifying which parts of the data should be analyzed and presented.

While planning an analytics approach and deploying the necessary tools, business managers need to carefully select data sets for further processing:

  • The data set must be relevant to provide answers to specific queries, meet defined objectives and help to arrive at data-driven decisions. 

  • The available data should be of a quality that will ensure that analytics yields accurate and conclusive results. 

By improving the data analytics in your company, the large volume of diverse information could filter into clear logic, resulting in sound business decisions. As the business environment evolves, the generated data streams also change. To make sense, the data analytics techniques must keep pace with the changing scenarios. 

So, how are you leveraging this power of data analytics in your company? Do you have the systems and technology in place to obtain accurate, effective, accurate, and usable results from analytics?

Here are some tips and tricks to improve data analytics at your company.

  1. Form the query. Simply put, ask the right questions. Design these queries to be clear, concise, measurable, and unambiguous. To improve analytics, frame the questions to include potential solutions.
    Let’s consider an example: a consulting business faces an increase in operating costs and is unable to bid competitively for prospective contracts. A well-designed query could be: will reducing staff costs help the company to bid in a more competitive manner?

  2. Study all the data, even if it seems irrelevant. Aim for a broad result rather than seeking a specific answer. Data arrives from a variety of sources, some of which are quite diverse and unrelated.
    Analyzing something very specific could result in missing out on another facet, which could be significant to the business. You are looking for a trend in your data and not a binary answer. Keep an open yet curious mind about the results, avoiding a narrow view. 

  3. Filter the content for relevance. Improving data analytics in your company involves separating valuable data from irrelevant information. This is a difficult step as data arrives from various sources and in several forms.

    Collating inputs from internal and external sources could help improve the signal-to-noise ratio for your analytical data. The internal sources could include customer feedback, market reports, and operational data. External sources could include websites, blog posts, social media and more.

    Heuristic logic derived domain expertise and analysis using time-series techniques will help distill useful content to quickly map trends and highlight changes in the market, issues with performance, or available opportunities.

  4. Define measurement metrics. This step selects the data set for measurement and its methodology with an aim to set limits and priorities.   

    • Select the parameters to measure. In the example of uncompetitive consulting business, you need to map the number of employees, their costs, and the time they spend in their assigned business activity. This study will raise additional queries about the utilization of employee time and, if implementing any processes, could improve productivity. The study should also consider the problems created by the reduction of staff strength — for example, mitigation of an unexpected increase in workload.

    • Select the measurement methodology. Planning for measurement methods is important. Ideally, this should be done before collecting the data to lend credence to your processes and analysis. Continuing with the example of the uncompetitive consulting business, this step involves defining metrics like the time frame (measuring for a quarter or on an annual basis), selecting the units for measurement (such as the unit of currency like USD or Euro), and selecting the various factors (only the annual salary or all costs, including the amount spent for staff welfare).

  5. Prove unique traits. After filtering for noise, the analyst has to ensure that the highlighted trends are really unique and not found in the normal distribution of occurrences. This study calls for confirming that the trait is missing in other similar groups and that the observation is absent from other similar products, distribution channels, locations, and competitors. Segregating the data into unique slots is essential before the perceived issue or trend is projected as actionable business intelligence. 

  6. Gather facts. After listing the queries and sorting the priorities, the analyst now works to collect information. Gathering cogent data has some associated ground-rules:
    • Before gathering new data, sift through the existing information and list the missing points. This will help in narrowing down the search for fresh material. 

    • Create a proficient set of people who will collect the new data and work together as an effective, multi-functional team. Agree on the naming convention and file storage protocols. This will save time, avoid rework, avert duplication of efforts and prevent loss of data. 

    • Create templates in advance for data collection, recording observations or conducting interviews. This will help implement consistency, enforce a structure, streamline efforts, and save time.        

    • Maintain a log of gathered information. Add all relevant information such as dates and notes about the sources. Include details of any data normalization processes done to the gathered information. This log will help authenticate the outcome of the study.

  7. Verify consistency. After establishing a conclusive trend in the presented data, the analyst must explain the reasons for the occurrence of a particular event. To deliver a logical outcome to the trend, study all the available facts, conduct historical research and unravel the reasons for this exclusive set of circumstances. Thorough knowledge of the domain will help to reach the right conclusions. 

  8. Work on the analysis. After collecting accurate and adequate data, then you can begin analyzing the data. Start by working the data in a variety of methods, such as plotting the figures and discovering interlinked properties through an Excel pivot table. The latter is an excellent tool to interactively sort and filter data using diverse variables.


    The pivot table allows calculation of data variables like mean, minimum, maximum and standard deviation. To get credible results, you will need to use data analytics tools, even though the humble and ubiquitous Excel program delivers usable conclusions and insights.
    As you work on the analysis, you may get affirmation about the correctness of your data. But usually, you may need to modify the initial queries and aggregate more information. This cycle of an initial analysis of information, trends, and correlation, followed by subsequent recalibration of queries and information, is essential to derive accurate and consistent results. All these activities help improve data analytics.

  9. Stay afresh. An agile approach that keeps pace with the fast speed of development is the only way to survive the information juggernaut. The business environment is transmuting rapidly and the data analytic solutions must also evolve at the same rate. Several data analytics projects fail, as they do not modify in real-time to close the gap between problems and the solutions.


    While some trends survive the test of time, several transform, often unpredictably. The result of your data analytics will be as good as the freshness and relevance of the data. Keep track of all the changes, however disparate they may be. For example, these changes may include a new product from your competitor, variance in the market scenario, presence of a business disruptor, availability of a new channel partner, a shift in national and international policy and similar.


    Stay updated with the newest internal and external data sets, and keep abreast with the latest tools and technologies to improve data analytics in your company. 

  10. Deliver conclusions. You have done extensive data analysis with historical and fresh data sets. Now is the time to deliver credible results. However, your work is not an absolute interpretation of hard facts, rather it is a scientific interpretation of theories and hypotheses. There is always a risk that some unknown factor or unanticipated chance could interfere with your conclusions. 

Before you deliver your final results, here is a checklist to verify your work:

  1. What was the initial query? Does your data set provide convincing replies to that query? How accurately does your work deliver solutions to the initial requirement?

  2. Will your work face criticism and objections? Do you have all the facts? How will you defend your data against any opposition?  

  3. Could there be any limitations to your conclusions? Are you aware of any inputs, methods or circumstances that you haven’t considered but could affect your data outcome? 

The checklist will help you evaluate your work and deliver a clearly focussed conclusion. 

These broad-based steps can help improve data analytics in your company. You and your team have domain expertise on the subject and have worked in a systematic way to reach decisions derived from scientifically collected data. The information is analyzed using proven software and applications. As you get more proficient, your data analysis will be consistent, faster, and accurate. These improvements in data analytics will allow your company to make informed decisions and maximize business potential. 

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