DATA ANALYSIS FOR SURVEYS: TURNING INSIGHTS INTO ACTIONABLE STRATEGIES
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INTRODUCTION
How can you maximize the benefits of a massive data set? What are the essential elements and what are they not? Is there standardized testing to ensure that your analysis is correct?
Feedback from your customers is invaluable. The rock walls are your raw insight, and the gold within them represents your vision for business and customer experience. The information you gain from this can transform your business.
Hitherto, determining which decisions are most effective based on information are significantly important. And as the data world becomes more complex and more and more companies collect massive amounts of data, more and more employees are needed who know exactly how to process data.
Features like automatic charts and graphs and word clouds help bring information to life. For example, sentiment analysis makes it possible to get an instant summary of people’s feelings based on thousands or even millions of open text responses. You can see positive, neutral and negative emotions and identify areas that need attention by viewing or filtering by emotion. You can get an even deeper understanding if you filter the question by sentiment. Imagine being able to turn all those text responses into a quantitative data set.
DEFINITION OF KEY WORDS
Data Analysis, survey, strategies.
What is Data analysis?
Data analysis is a systematic method that uses statistical and/or logical techniques to describe and summarize data so that others can evaluate it for themselves. Data analytics is key in today’s data driven world. Organizations can use this information by providing it. make informed decisions, optimize processes and gain a competitive advantage. By transforming raw data into meaningful insights, data analytics can help companies identify opportunities, reduce risks and improve overall performance.
TYPES OF DATA ANALYSIS
Descriptive analysis is the initial stage of data analysis. Describe the purpose of descriptive analysis by asking, “How much money did you make in May? Or how many children between 2 and 10 go to school?
Diagnostic analytics – Why did this happen?
When you have an overview of what is in your data and what your sample looks like, you might want to know why certain things are happening. Maybe you found that in one district, far less children attend school than in the other districts. Could there be something in your data that shows other ways in which this district is different? With diagnostic analytics, we can go one step deeper and ask the question: Why did this happen?
Prescriptive analytics – What should be done?
Now that you have an idea of what is likely to happen, you might want to know what the best course of action is. Prescriptive analytics tries to answer the question: What should be done? or what can we do to make happen? Prescriptive analytics is mostly used in large companies that are looking for advice on, for example, their inventory or supply chain. It goes one step further than descriptive and predictive analytics by recommending possible outcomes. Essentially, you can predict multiple futures and allow companies to assess a number of possible outcomes based upon their actions.
Predictive analytics – What is likely to happen?
By the time you know why something happened, we might go as far as predicting what is likely to happen next, given our knowledge of previous events. Predictive analytics tries to answer the question: What is likely to happen? By using what we learned with descriptive and diagnostic analytics, we can use predictive analytics to look at clusters, tendencies or maybe exceptions that allow us to make a certain prediction.
What is Survey?
A survey is a method of gathering information using relevant questions from a sample of people with the aim of understanding populations as a whole. In other words, survey means to take a general or comprehensive view of or appraise, as a situation, area of study, etc. to view in detail, especially to inspect, examine, or appraise formally or officially in order to ascertain condition, value, etc. to conduct a survey.
What is Strategy?
Strategy is a plan of actions that fit together to reach a clear destination. That destination is dictated by a set of decisions that sets the organization apart from its competitors, derives from the organization’s unique characteristics, and is hard to emulate.
IMPORTANCE OF DATA
Data helps you make better decisions: data is synonymous with knowledge and when you use data to back your decisions, you avoid assumptions, mistakes, and bias, helping your decisions be better overall.
Data helps you achieve your goals: the best designed strategies are ones that have data behind them to properly evaluate the success of the strategy; by using data, you’ll be able to plainly and clearly see what’s working, what needs tweaking, and what isn’t working at all.
Data allows you to be strategic: with clear answers as to what’s working, where your money is going, and what clients are liking, you’ll be able to be more strategic with your planning and decision-making, saving time and resources across the board.
Data helps you avoid problems later on: if you’re constantly collecting data, you’ll be able to monitor how things are working and solve issues on the fly while they’re still minor instead of waiting for them to become major.
Data helps back you up: if you want to propose a change or adjustment to your boss, you’ll have to explain why. And there’s no better way to prove a point than with numbers and data that clearly back your ideas up.
MEANING OF RAW DATA
The conversion of raw data into actionable insights can only be achieved through a comprehensive understanding of the concept of “raw data
Raw data is the data that has been collected before being cleaned, analyzed or organized. Let’s get started. It refers to the entire set of data, regardless of if it’s been collected from various sources, and can take practically any form: databases, spreadsheets, images, videos, survey results, and more.
Although it may seem like any data could prove to be useful in some capacity, raw data isn’t a random compilation of information. On the contrary, skilled data professionals know how to collect raw data that will be useful later on. When beginning your data collection process, make sure you follow these three steps:
Choose your data: If you’re looking for business statistics, you’ll probably need to turn to financial reports or market research for valuable data. On the other hand, if you’re looking to improve the overall client experience, customer surveys may be your best bet.
Start collecting data: Once you have established your targets and methods, commence accumulating it. There is no need to be anxious: you will likely receive a substantial amount of information, but you can scrutinize significant details in the analysis process at varying intervals.
Clearcut your objectives: How should you go about analyzing the data? This will enable you to organize your data collection and ensure that your set is effective.
What are Actionable Insights?
Insights are information you’d get from looking at data, right? The answer is yes: that’s exactly what insights are. But the truly valuable insights and information collected from data are referred to as actionable insights, highlighting the ability to take action from that information and make improvements. Generally speaking, there are two types of actionable insights:
No matter which kind of actionable insight you’ve created, there’s an important step required to make that insight truly valuable; it isn’t enough to just have data in front of you. You must engage in thoughtful and deliberate data analysis to find these actionable insights. To be actionable, insights must be the following:
Specific: general statements won’t be very helpful here; it’s important that your insights are incredibly specific and focused on an issue itself to help you solve it.
Credible: without a trustworthy source, your data won’t be credible and is therefore rendered useless. Make sure your data is clean, properly analyzed, and taken from quality sources.
Relevant: actionable insights aren’t valuable if they’re outdated or address an issue that can’t be dealt with just that; your actionable insights need to relate directly to current issues that your company is facing.
Based on data: this is probably quite self-explanatory, but it’s important to highlight that actionable insights must, with no exceptions, be data-based. You can obtain this information from surveys, market research, or reports, but there must be facts behind your actionable insights.
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BASIC DATA ANALYSIS TECHNIQUES
Regression analysis
Predictive analysis
Descriptive analysis
Cluster analysis
Content analysis
Diagnosis
Monte Carlo simulation
Prescriptive analysis
Time
Factor analysis
Narrative
Cohort analysis
Statistics
Data mining
Grounded theory
Text analysis
Discourse
Sentiment analysis
Exploratory data analysis
Inferential analysis
Decision-making
Data analysis
Data collection
Qualitative analysis
MEANING OF DATA VISUALIZATION
Data visualization is the graphical representation of information and data.
By utilizing visual aids like charts, graphs, and maps, data visualization tools can facilitate the exploration of trends, deviations from expected values, or patterns. This is an easy task. Moreover, it presents an excellent means for employees or business owners to convey information without any disruption to a general audience.
IMPORTANCE OF DATA VISUALIZATION
Whether simple or complex, the right visualization can bring everyone on the same page, regardless of their level of expertise. Data visualization helps people see, interact with, and better understand data.
The better you can convey your points visually, whether in a dashboard or a slide deck, the better you can leverage that information. The concept of the citizen data scientist is on the rise. Skill sets are changing to accommodate a data-driven world. It is increasingly valuable for professionals to be able to use data to make decisions and use visuals to tell stories of when data informs the who, what, when, where, and how.
While traditional education typically draws a distinct line between creative storytelling and technical analysis, the modern professional world also values those who can cross between the two: data visualization sits right in the middle of analysis and visual storytelling.
It’s hard to think of a professional industry that doesn’t benefit from making data more understandable. Every STEM field benefits from understanding data, and so do fields in government, finance, marketing, history, consumer goods, service industries, education, sports, and so on.
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TYPES OF DATA VISUALIZATIONS
Graph: A diagram of points, lines, segments, curves, or areas that represents certain variables in comparison to each other, usually along two axes at a right angle.
Geospatial: A visualization that shows data in map form using different shapes and colors to show the relationship between pieces of data and specific locations. Learn more.
Infographic: A combination of visuals and words that represent data. Usually uses charts or diagrams.
Dashboards: A collection of visualizations and data displayed in one place to help with analyzing and presenting data.
Chart: Information presented in a tabular, graphical form with data displayed along two axes. Can be in the form of a graph, diagram, or map.
Table: A set of figures displayed in rows and columns.
DATA VISUALIZATION TOOLS
Tableau
Power bi
Datawrapper
Infogram
Zoho Analytics
Qlik
Google Charts
Sisense
Plotly
Looker
Domo
Fusion Charts
Pie chart
Grafana
Highcharts
Microsoft Excel
Histograms
Maps
Open Heatmap
RAWGraphs
Scatter plots
Chart Blocks
Visme
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HOW TO TURN RAW DATA INTO CLEAN ACTIONABLE INSIGHTS
To make your data set useful, you’ll need to follow a few steps to make sure there are no errors or issues that will make your outcomes incorrect. To process your raw data, you’ll:
Prepare the data: during this initial step, you’ll check for any errors or invalid values and ensure that all data is in the same format (if you’ve collected data from various sources, it may take a bit to unify all values).
Translate the data: no, we don’t mean to translate the data into a different language! Here, data translation means making sure it’s readable for a machine to process it. If your data was collected online, you’ll have an easier time than if it’s manually collected, but double check your file format is correct before diving right in.
Process the data: the data will go through various machine learning algorithms that are specifically instructed on how to make the most from the data. Here, patterns, trends, relationships, and problem areas will be highlighted.
Visualize the data: you’ll be able to organize and display your clean, understandable data set in a variety of formats. Think about what you’re trying to portray and make sure you pick the visualization method that is right for your exact situation.
Store the data: last but definitely not least, you need to respect local and international privacy regulations, properly storing and securing the data you used in your analysis. Ensure your company’s storage policies are in line with industry standards and don’t be afraid to explore cloud storage options.
IMPORTANCE OF ACTIONABLE INSIGHTS
Now that you understand what makes insights actionable and why they’re so important, it’s time to dive into the actual process of drawing actionable insights from your data. And how can you do that? Let’s explore:
Actionable insights allow you to better understand your clients: at the end of the day, you’re trying to sell a product or service to a customer and as competition grows practically daily, unhappy customers will simply turn to the next option. With actionable insights as to why clients are leaving your company and looking to fill their needs elsewhere, you’ll be able to address those specific problems and hopefully convince more clients to remain loyal in the future.
Actionable insights help you stay ahead of the competition: we mentioned competition is popping up left right and center and it’s true–that’s why you need to be on top of what your competitors are doing and make sure you’re offering comparable services or better alternatives to help foster customer loyalty.
Actionable insights help you grow: if you don’t know where your problem areas are or what actions you’re taking that are working well, how will you improve your business strategy? There are so many different moving parts in a company that it can be almost impossible to truly know what is working well and what needs to be adjusted. With actionable insights, you’ll have that answer clearly defined.
Data drives business and there’s no arguing with that. In fact, we’d go so far as to say that it’s the driving force behind the vast majority of companies and that’s why we’re focused on creating the next generation of data professionals that are ready to take on the challenges posed by collecting and analyzing large amounts of data.
CONCLUSION
Data analytics is germane in today’s data driven world. Organizations can use this information by providing it, make informed decisions, optimize processes and gain a competitive advantage. By transforming raw data into meaningful insights, data analytics can help companies identify opportunities, reduce risks and improve overall performance.
Get Started
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Tel: (+234) 802 320 0801, (+234) 807 576 5799
E-Mail: info@qeeva.com
FAQ
Why is it important to turn insights into actionable strategies?
Data and insights are valuable, but only if they lead to concrete actions. By turning insights into actionable strategies, you can:
- Help your business grow: data-driven and insightful decisions can lead to more effective marketing campaigns, product development and operational improvements.
- Improve decision-making: insights provide a clear picture of the situation, enabling informed choices to succeed.
- Increase the return on invested capital: Information-based activities can make the use of resources more efficient and improve the return on invested capital.
- Gain a competitive advantage: Statistics help you identify opportunities your competitors are missing, giving you a strategic advantage.
- Increase employee engagement: When employees see insights turned into action, they feel like their work has a tangible impact.
At what stages are insights transformed into effective strategies?
- Define your goals: What do you want to achieve with your insights? Increased sales, improved customer satisfaction or brand awareness?
- Prioritize your reviews. Not all reviews are created equal. Identify the most effective insights that fit your goals.
- Develop Action Plans: Brainstorm and develop actionable actions for each priority review.
- Define resources and ownership: Allocate resources and assign clear responsibilities to implement action plans.
- Set Measurable Goals: Set metrics to track strategy progress and effectiveness.
- Monitor and adjust: Monitor results regularly and be prepared to adjust your strategies based on new information or changing circumstances.
What are the most common challenges to turning insights into action?
- Analysis Paralysis: Getting stuck analyzing data and overthinking instead of acting.
- Lack of clear goals: Without clear goals, it is difficult to determine which knowledge to prioritize and act on.
- Resistance to change: People may be hesitant to change existing processes or workflows based on new knowledge.
- Lack of resources: Implementation of new strategies may require additional resources, such as budget or personnel.
- Difficulty measuring results: Choosing the right metrics and tracking progress can be difficult.
How can I overcome these challenges?
- Set deadlines and decision points: force yourself to move from analysis to action in a certain time frame.
- Communicate effectively: Clearly communicate your views and rationale for your proposed strategies to stakeholders.
- Focus on small wins: Start with smaller, achievable actions to accelerate data-driven decision-making and prove its value.
- Secure buy-in from managers: Get buy-in from managers to secure resources and staff to implement change.
- Choosing the right data: Choose metrics that directly relate to your goals and provide insights you can use to improve.
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