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Data Driven Decisions: Full Guide

No one wants to make mistakes, especially when doing business. A data driven decision making is the most promising, but not easy, way of cutting losses and increasing victories. The companies, which build corporate culture based on data receive a competitive advantage, that is why we hear a lot about data analysis, data driveness, data science, data visualization, predictive modeling, forecasting, scorings and so on. In this article, we will show you step-by-step how incorporate data driveness into your organization, adopt a data driven strategy, and not fall into the trap of gut-based decision making.

Main Problems Which Data Driven Approach Eliminate

  • Decrease Confirmation Bias: a habit of using all the data helps overcoming a tendency of looking for the information that confirms preexisting opinions.
  • Lower Attention Bias: by grounding themselves in data driven results, people wisely prioritize tasks and stop cherry-picking the ones that seem important.
  • Replace Optimism with Realism: as a result of data driven approach, optimistic evaluations of a person change to more realistic ones, which helps in combating overestimating the chance of a positive outcome and to underestimating the negative ones.
  • Anchoring: The ease of obtaining data-driven insights from various information sources allows for performing an accurate analysis and not just using the most obvious data while ignoring all the rest.
  • Reliance on HIPPO: (HIPPO – highly paid person opinion) ideas are evaluated depending on data, regardless of the author.

What Stops Companies from Adopting Data Driven Methodology?

For the most part, the enterprises face two following problems:

  1. People: the first thing that limits companies is an unawareness of data-driveness and the integration methods for employees to pick up. It’s difficult to create the full-scale practical data driven methodology without understanding its connection to the working process.
  2. Technology: with a range of solutions on the market, the enterprises may pick the unsuitable offerings or face difficulties with the introduction of data storage, data analysis, and data visualization in business process.

Both of these problems are not set and steady and can be solved with training of the employees or the recruitment of specialists from the market.

Steps for Incorporating the Data Driven Decisions in Business

Step 1: Collect the Data

To determine which data storage is better to use, you need to answer the following questions:

  1. What type of data do you want to collect?
    • Structured: Simply speaking, it’s a set of interconnected tables. The typical examples of databases include PostgreSQL, MySQL, HP Vertica. They are convenient because data can be highly accurate and provide a complete picture of the situation. One can obtain data from such databases quickly, which is a big plus for analytics.
    • Unstructured: There is no need to worry about the architecture of databases in advance, and immediately start collecting data. The typical examples of databases, include Hadoop, MongoDB. Because the analysis of unstructured data is harder and requires a competence, such a knowing the programming languages, businesses have more troubles managing it comparing with the old-school structured data.
  2. What volume of data do you plan to store?
  3. Where are you going to keep this data?
    • Buy a Dedicated Server. With dedicated servers, businesses use the IT capacity and expertise to manage ongoing maintenance, patches and upgrades. It’s reasonable when you have heavy I/O applications, such as big data platforms.
    • Buy a Cloud Server or Cloud Storage. With cloud servers, you can optimize IT performance without the huge costs associated with purchasing and managing fully dedicated infrastructure. Because the cloud is a one for all solutions, there are concerns associated with vulnerability to attacks, speed of data management, lifetime costs, and scalability.
  4. Who will backup your data infrastructure?
    • Outsource
    • A dedicated team within the project
  5. How much is it going to cost?

Step 2: Integrate the Information for Data Driven Decision Making

After you collected the data, you need to think of how you would deliver data-driven insights to your team. In general, the more people have access to information, the better it is for the whole organization (unless you are working in Pentagon or CIA).
There are multiple business intelligence tools, such as Excel, Tableau, Matplotlib that can pull together complex sets of data and present it in an understable way. It’s worth mentioning that the way you present the data determines how much you gain from them. In article “Data visualizations with Tableau” we described in details how data visualization impacts enterprises.
Below, is the listing of data visualization tools for forming data-driven design, divided by skill:

Simple Visualization

Excel

Analogs: Google Sheets, Libreoffice

Required Skills: Common

Excel Demo Data Visualization Screenshot

Excel works well with simple charts and is convenient for doing what-if analysis.
If you need sophisticated visualization, it’s possible to connect Excel with Power BI or Tableau.
Additionally, Excel works with CSV files, which convenient because you can upload the data from your tables in data storages, such as in MySQL, PostgreSQL, HP Vertica, and others).

Advanced Visualization

Tableau

Analogs: Power BI, Qlikview, Spotfire

Required Skills: Medium/Advanced

Tableau Demo Data Visualization Screenshot

Tableau has very good BI tools for analysing  data.
Tableau has useful core features, such as:

Super Custom Visualization

Matplotlib

Analogs: ggplot2, bokeh, plot.ly

Required Skills: Advanced

Matplotlib Demo Data Visualization Screenshot

Matplotlib is often used for visualizing data when writing the programming for data science, machine learning projects.
Matplotlib also contains a lot of advanced toolboxes and offers great visualization libraries.

Step 3: Hire the right specialists for developing Data Driven Methodology

Who are those people, and how should they be organized under the corporate structure?

  • Data Analysts

    They range from the entry-level jobs, which are usually more concentrated on gathering data and preparation, to highly-specialized and skilled analysts. Such workers are typically focused on a vast array of areas, such as loyalty programs, e-mail marketing, certain elements of stock market, and are domain experts by definition. The specific roles in an enterprise is dependent on the business’s scale as well as market, maturity, and domain. In any scenario, the output of data analysts would likely be a mix of analysis and data driven reports.
    Necessary skills: Excel, SQL.

  • Data Engineers and Analytics Engineers

    They are responsible for obtaining, cleaning, and munging data and getting it into a form that analysts can access and analyze. Data Engineers and Analytics Engineers also handle the operational concerns, such as throughput, scaling, peak loads, and logging, and building business intelligence tools.
    Necessary Skills: HadoopHive, Pig, Sqoop, Flume, Spark SQL.

  • Data Scientists

    They are the people who hold advanced degrees in quantitative subjects and are inclined to solve complex problems through building big data projects. They are responsible for building data products, including building recommendation engines using machine learning, doing natural language processing, and predictive modeling.
    Necessary Skills: Spark MLlib, PySparkHadoop, Ignite, Spark, Hive, Kafka.

  • Statisticians

    They are skilled personnel who focus on statistical modeling across the organization and have at least a master’s degree in statistics. Statisticians are responsible for the data driven approach and handle design of surveys, experiments, and collection of protocols to obtain the raw data. As for the industries, statisticians are often used in insurance, healthcare, research, and government.
    Necessary Skills: Excel, Word, SAS, R, LaTeX.

  • Quants

    They are mathematically skilled quantitative analysts who typically work in the financial services sector modeling securities, risk management, and stock movements on both the buy and sell side of the market. Quants usually help with data driven strategy and hold mathematics, physics, engineering degrees. Some of them are especially strong programmers in languages that can process data and generate actions with low latency.
    Necessary Skills: R, C++, C#.

  • Accountants and Financial Analysts

    They range from entry-level positions, from short term financial planning and analysis, to advanced jobs, which requires thorough budget process, providing analysis, research, and data driven automation in some cases. They are experts that make data driven decisions and focus on internal financial statements, audit, forecasting, analysis of business performance.
    Necessary Skills: ExcelWord.

Step 4: Start Building the Data-Driven Culture

Data Driven Decision Making Step 4 Building Culture

There are 3 Main Ways to Reinforce the Data-Driven Culture Inside of Your Enterprise:

  1. Find the allies. Come, show, tell and convince why it will be better. Enlist the support of colleagues.
  2. Share the information. It’s necessary to provide a broad access to data, which ultimately involves sharing the information among business units, teams, and individuals, regardless of the relative scale of the employee. Also, you would have to ask people uncomfortable questions, such as: “Based on what information was the decision made? Where is this info? Would you show it to me?”
  3. Integrate the information for producing data-driven decisions. The KPI and OKR frameworks helps you to encourage your employees to draw hypotheses, build experiments, and learn from the results.

Benefits of the Data Driven Decision Making

  1. On average, the most data-driven companies found to be 4% more productive and 6% more profitable, if comparing with the counterparts.

  2. The majority of the Fortune 1000 companies have been able to cut losses by employing data-driven business strategies.

Similarly, data-driven management gives framework for solving the critical business problems, such as:

  • Smart Task Prioritization
  • Customer Understanding
  • Transparent communication between employees and departments
  • Strengthening of the creativity and field for innovation

Conclusion

Launching the data-driven processes in the organization is hard, and doing this in a quality manner requires a lot of expertise. If you have any questions, contact us, and we will gladly share our knowledge to help you in the matter.

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