Cheat Sheet

Big Data for Small Business For Dummies Cheat Sheet

From Big Data For Small Business For Dummies

By Bernard Marr

Big data makes big headlines, but it’s much more than just a buzz phrase or the latest business fad. The phenomenon is very real and it’s producing concrete benefits in so many different areas – particularly in business. Here you will get to the heart of big data as a business owner or manager: You will take a look at the key terminology you need to understand the crucial big data skills for businesses, ten steps to using big data to make better decisions, and tips for communicating insights from data to your colleagues.

Understanding Big Data Jargon

The technical jargon surrounding big data can seem a little daunting at first. The key phrases and terms you’re likely to come across, with easy-to-understand definitions for each, follow:

  • Big data: Increasingly, everything you do leaves a digital trace (or data), which you (and others) can use and analyse. The phrase big data refers to that data being collected and the ability to make use of it.

  • Big data analytics: This is the process of collecting, processing and analysing data to generate insights that inform fact-based decision making. In many cases it involves software-based analysis using algorithms.

  • Algorithm: A mathematical formula or statistical process run by software to analyse data. It usually involves multiple calculation steps and can be used to automatically process data or solve problems.

  • Cloud computing: Software or data running on remote servers, rather than locally. So instead of storing or computing things on your own machine, you can use other computers that are connected to your computer via a network (such as the Internet).

  • Structured data: Any data or information located in a fixed field within a defined record or file, such as a database or spreadsheet. Its inherent structure makes it quick, easy and cheap to analyse.

  • Unstructured data: All the data not easily stored and indexed in traditional formats or databases. It includes email conversations, social media posts, video content, photos, voice recordings, sounds and so on. Its lack of structure makes it more difficult to analyse using traditional computer programs.

  • Semi-structured data: You guessed it, this is a cross between unstructured and structured data. It’s data that may have some structure that can be used for analysis but lacks the strict structure found in databases or spreadsheets. For example, a Facebook post can be categorised by author, date, length and even sentiment, but the content is generally unstructured.

  • Internal data: This accounts for all the data your business currently has or could potentially access or generate in future. It could be structured in format (for example, a customer database) or it could be unstructured (conversational data from customer service calls).

  • External data: Put simply, this is the infinite array of information that exists outside your business. It can be publically available or privately held and it can also be structured or unstructured in format.

  • The Internet of Things: A network that connects devices (the things referred to in the name) so that they can communicate with each other. This encompasses technology like smart televisions, smart phones, and sensors, and it’s all possible thanks to the massive increase in connectivity between devices, systems and services.

6 Key Big Data Skills Every Business Needs

What are the key skills required to use big data successfully? The list here includes six key skills that all businesses should develop, either through recruiting data scientists who match these attributes, or by developing these skills in existing employees:

  • Analytics: This involves determining which data is relevant to the question you’re hoping to answer and interpreting the data in order to derive those answers. Key skills include a knack for spotting patterns and establishing links, the ability to make sense of a range of data (both structured and unstructured) and a sound knowledge of industry-standard analytics packages like SAS Analytics and Oracle Data Mining.

  • Creativity: Anyone can be formulaic – you need to aim for innovation that will set your business apart from the pack. Creativity is especially important for any business hoping to make sense of unstructured data – data that doesn’t fit comfortably into tables and charts. Valuable creative skills include a knack for problem solving (perhaps even spotting problems others aren’t yet aware of) and the ability to come up with new ways of gathering and interpreting data.

  • Maths and statistics: People with a strong background in maths or statistics have a good grounding for big data-related work. You’re looking for at least a basic grasp of statistics and the ability to wrangle messy data into figures that can be quantified so that you can draw conclusions from them.

  • Computer science: This very broad category covers a whole range of subfields, such as machine learning, databases and cloud computing. It may cover everything from plugging together the cables to creating sophisticated machine learning and natural language processing algorithms. Key skills include a solid understanding of database technology and a firm grasp of technologies such as Hadoop, Java and Python.

  • Business acumen: People who work with big data need a firm grasp of the company’s goals and objectives, as well as an understanding of whether the business is heading in the right direction. This includes understanding what makes the company tick, what makes it thrive and why it stands out from its competitors (and if it’s not thriving, why it’s not).

  • Communication: You can have the best analytical skills in the world, but unless you’re able to present findings in a clear way and demonstrate how they can help to improve performance and drive success, all that analysis will go to waste. Great interpersonal and written communication skills are vital, as is the ability to add value to data through insights and analysis. A knack for storytelling and being able to bring data to life through visualisation techniques will also help immensely.

10 Steps to Using Data to Improve Business Decisions

Data should be at the heart of strategic decision making in business, whether you run a huge multinational or a small family-run business. Big data can provide insights that help you answer your key business questions, such as ‘How can I improve customer satisfaction?’. Data leads to insights; business owners and managers can turn those insights into decisions and actions that improve the business.

Use this ten-step process for making data-based decisions:

  1. Start with strategy.

    Instead of starting with what data you could or should access, start by working out what your business is looking to achieve. In a nutshell, you need to work out what your strategic goals are, for example, increasing your customer base.

  2. Hone in on the business area; identify your strategic objectives.

    Identify the areas most important to achieving your overall strategy. For most businesses, the customer, finance and operations areas are key.

  3. Identify unanswered questions.

    Work out which questions you need to answer in order to achieve those goals. By working out exactly what you need to know, you can focus on the data that you really need.

  4. Find the data that will help answer those questions.

    Focus on identifying the ideal data for you – the data that could help you answer your most pressing questions and deliver on your strategic objectives.

  5. Identify what data you already have or have access to.

    After you identify the data you need, it makes sense to see if you’re already sitting on some of that information, even if it isn’t immediately obvious.

  6. Work out if the costs and effort are justified.

    Only after you know the costs can you work out if the tangible benefits outweigh those costs. In this respect, you should treat data like any other key business investment. You need to make a clear case for the investment that outlines the long-term value of data to the business strategy.

  7. Collect the data.

    Much of this step comes down to setting up the processes and people to gather and manage your data. You may be buying access to an analysis-ready data set, in which case there’s no need to collect data as such. But, in reality, many data projects require some amount of data collection.

  8. Analyse the data.

    You need to analyse the data in order to extract meaningful and useful business insights. After all, there’s no point coming this far if you don’t then discover something new from the data.

  9. Present and distribute the insights.

    Unless the results are presented to the right people at the right time in a meaningful way, then the size of the data sets or the sophistication of the analytics tools don’t really matter. You need to make sure the insights gained from your data are used to inform decision making and, ultimately, improve performance.

  10. Incorporate the learning into the business.

    Finally, you need to apply the insights from the data to your decision making, making the decisions that will transform your business for the better – and then acting on those decisions. For me, this is the most rewarding part of the data journey: turning data into action.

How to Communicate Insights from Big Data

Big data can help you gain insight. Businesses gain competitive advantage when the right information is delivered to the right people at the right time. This means extracting insights and information from data and communicating them to decision makers in a way they’ll easily understand. After all, people are less likely to act if they have to work hard to understand the information in front of them.

Make sure your insights shine through with these top tips:

  • Identify your target audience. Who your audience is depends on your strategic questions. The audience may be you if you’re the business owner, or it could be your human resources team, your marketing team or a combination. Ask yourself who’s going to see these results. What do they already know about the issues being discussed? What do they need and want to know? And, what will they do with the information?

  • Customise the information for your audience. Be prepared to customise your information to meet the specific requirements of each decision maker.

  • Remember what you’re trying to achieve. Try not to get distracted by interesting insights that have nothing to do with answering your strategic questions and achieving your business goals. There may be scope to revisit those other insights in future but, for now, focus on what you set out to achieve.

  • Avoid creating a wall of text. Remember that data can be presented as a number, a short written narrative, a table, a graph or a chart. In fact, the best approach is likely to involve a combination of these formats.

  • Use data visualisation techniques. Visuals are great for conveying information because they’re quick and direct, they’re (usually) easy to understand, they’re memorable and they add interest, being much more likely to hold the reader’s attention than a full page of text.

  • But don’t neglect the text. Numbers, charts and visuals may only give a snapshot; narrative allows you to embellish on key points. Use short narratives to introduce what you’re showing and highlight the key insights.

  • Use clear headings to make the important points stand out. This way, even at a quick glance, the key points will be obvious.

  • Link the information to your strategy. If you’re presenting information that directly answers a strategic business question, such as ‘How do we reduce staff turnover by ten per cent?’, include that question in the opening narrative and maybe even the headline.