Getting familiar with these essential terms will help you and your teams, regardless of technical ability, feel confident talking about data and understanding how to use it to create business value.

Generative AI Terms by Topic

Batch processing

Batch processing is when a computer automatically runs a repetitive task or group of tasks on a large amount of data, processing it as a single unit rather than a series of separate jobs. Certain processor-intensive tasks can be inefficient to run individually; with batch processing, the data jobs are run together, often at an off-peak time to conserve computer resources.

• What it means for customers: When jobs like order processing are run as a batch, customers experience quicker turnaround times than when those tasks are handled individually, as well as more consistent and accurate results. 
• What it means for teams: Teams save time by minimising the overhead required for individual tasks, and gain more consistent quality control by using standard business rules across a batch process.

Business analytics

Business analytics is the practice of using data to test hypotheses and make predictions or more informed decisions, often around future performance. Business analytics is predictive, which means you model and analyse data to identify new insights and anticipate trends.

• What it means for customers: Customers get improved experiences across the board, including personalised product recommendations and just the right marketing messages, delivered at just the right time.
• What it means for teams: Teams get ahead of the curve with business analytics, using it to help them create more accurate predictions, and make smarter decisions about resource planning, demand forecasting, risk assessment, and more.

Business intelligence

Business intelligence is the practice of bringing together large amounts of data to view a current snapshot of performance, and pulling actionable insights to drive decisions. Business intelligence is descriptive, which means it “describes” what’s happening at a particular moment in time.

• What it means for customers: When organisations can see how their past or current efforts have (or haven’t) worked, and use these insights to make improvements, they’re better able to serve their customers, which leads to an increase in customer satisfaction and loyalty.
• What it means for teams: Teams use business intelligence for internal efforts, like tracking key performance indicators (KPIs), and external efforts, like spotting business risks within departments or teams, such as monitoring customer satisfaction (CSAT) scores.

Customer data platform (CDP)

A CDP helps businesses collect, organise, and use customer data from sources like websites, mobile apps, emails, and social media to build unified profiles of their customers.

• What it means for customers: With a CDP, companies can better anticipate customer needs for more meaningful brand interactions that help solve customer problems.
• What it means for teams: With a unified view of their customers, teams can create more meaningful and targeted experiences, campaigns, and products. They’re also better able to track, measure, and improve as more data comes in.

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Dashboard

A dashboard is a visual display of data used to monitor conditions or facilitate understanding. Dashboards generally include multiple interactive charts describing important business processes and KPIs.

• What it means for customers: organisations that can monitor their processes effectively can produce targeted insights that better serve customers’ needs.
• What it means for teams: By monitoring progress towards key business goals, dashboards let people see positive and negative trends, and drill into the reasons that cause them, allowing them to take action.

Data and big data

Data is the raw facts, figures, and other information, like customer names and contact details, that organisations collect, store, and analyse. Data can come from different sources, like customer interactions, surveys, sensors, and social media. Big data means large and complex amounts of information. The five V’s of big data — volume, velocity, veracity, value, and variety — describe the challenges of storing, governing, and analysing it in structured, unstructured, and semi-structured forms.

• What it means for customers: When companies use big data, customers not only receive more tailored and relevant messaging. They also benefit from increased security and trust, since big-data analysis spots patterns that identify fraudulent behaviour.
• What it means for teams: Teams use data to create better customer interactions. They collect and analyse data related to past purchases, browsing behaviour, and other data points to recommend specific products or services. That enhances customer experience and increases the odds of a purchase.

Data analytics

Data analytics is the science of examining raw data to draw conclusions. It includes tools and technologies that make it easier to understand, aggregate, and visualise data.

• What it means for customers: Customers experience the benefits of data analytics when they engage with improved products and services.
• What it means for teams: Teams use data analytics for continuous improvement across key functions like customer service, product development, marketing, and more.

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Data culture

A data culture is the shared behaviours and beliefs of individuals who advocate for and prioritise using data to enhance decision-making. A data culture empowers everyone, not just data analysts, to unlock and create business value with data.

• What it means for customers: When every person in the organisation is empowered to use data, everyone can make smarter decisions about what the customer needs.
• What it means for teams: Teams solve problems faster. With data at the forefront, they get ahead of trends, create more tailored offerings, improve forecasting, and more.

Data governance

Data governance is the framework organisations use to define the rules and responsibilities for effective handling of data throughout its lifecycle to ensure its reliability and relevance. These rules define processes and protocols to maintain usability, quality, policy compliance, privacy, and security.

• What it means for customers: Having reliable and relevant data is essential to creating quality customer experiences. Plus, customers are more likely to trust an organisation that demonstrates respect for their privacy rights and sensitive personal information.
• What it means for teams: Teams have more peace of mind knowing that data is reliable and relevant, and that clear standards and practices exist to protect data to reduce the likelihood of a breach.

Data harmonisation

Data harmonisation is the process of bringing together data from multiple sources to create a unified dataset that functions as if it were a single data source. It involves aligning data elements, formats, and structures to eliminate inconsistencies and make the data easier to compare and analyse.

What it means for customers: Customers get a consistent experience across departments because organisations can access data, like customer preferences and purchase history, from various sources as if it were a single source.

What it means for teams: Teams have a more holistic view of customers and can access and analyse information more quickly, without having to access multiple systems.

Data insights and real-time insights

Data insights are key findings, like data patterns and trends, that you get from data analysis. Real-time insights are the immediate and up-to-date information from data analysis that comes in the moment an event occurs, such as sales through an ecommerce site. You can use these insights to guide decision-making and strategies.

• What it means for customers: Data insights lead to key customer benefits across all brand interactions, including more tailored products and services, and proactive support. With real-time insights, organisations can create real-time personalisation, more targeted marketing, and nearly immediate responses to issues.
• What it means for teams: Teams build a competitive advantage in the market, using data insights to gain a deeper understanding of customers, improve processes, and fuel smart decisions.

Data lake

A data lake is a centralised storage repository of raw data. It’s a vast, flexible, and low-cost storage system organisations use to collect and store large volumes of structured, unstructured, and semi-structured data in its original format. Data lakes capture a wealth of unstructured data like social media posts, sensor logs, and location data.

• What it means for customers: With the immense information available in a data lake, brands can anticipate customer wants and needs.
• What it means for teams: Teams access a huge amount of data in a single space, allowing them to move faster and keep up (or get ahead of) competitors.

Data lakehouse

A data lakehouse has the scalability and flexibility of a data lake, and the structure and governance of a data warehouse — the best of both worlds. Because of this hybrid quality, organisations can quickly and easily extract insights from all their data, regardless of format or size.

• What it means for customers: Customers benefit from unified omnichannel experiences, quicker response times, and improved data security.
• What it means for teams: Teams eliminate the need for separate data storage and processing structures, which allows them to unify historical and real-time data all in one place.

Data literacy

Data literacy is the ability to explore, understand, and communicate with data.

• What it means for customers: Customers want to know brands understand them and can help them solve their problems. organisations that embrace data literacy can have this kind of in-depth knowledge across customer touchpoints.
• What it means for teams: Teams with strong data literacy skills build personal, professional, and organisational growth, increasing critical thinking, career opportunities, and data-driven success.

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Data masking

Data masking is the process of replacing sensitive data with fictitious or anonymised data to protect sensitive or private information and to comply with privacy requirements. Data masking is used in training or testing scenarios when real data is not needed, or when sharing data with third parties. You can also use masking to ensure you’ve eliminated all personal data when writing AI prompts or training an AI model.

What it means for customers: Customers feel more confidence when companies protect sensitive and personally identifiable information.

What it means for teams: Teams can easily follow privacy requirements while still having functional data to use in testing, training, or development.

Data mining

Data mining is the process of discovering patterns in large datasets. It uses techniques like machine learning, statistics, and database systems to turn raw data into useful information.

• What it means for customers: Your customers get predictive recommendations about what they want and need, often before they know they need it. Customised recommendations, reminders, and add-on product offerings are all powered by data mining.
• What it means for teams: A deeper understanding of customer behaviour keeps all your marketing and sales strategies efficient and effective.

Data science

Data science is a field that combines scientific methods, statistics, algorithms, and data mining techniques to generate insights from structured and unstructured data.

• What it means for customers: Customers experience faster service and improved personalisation with data science tools like recommendation algorithms, which provide tailored suggestions and machine learning algorithms that automate specific support tasks.
• What it means for teams: Teams use data science to continually improve and iterate on service and product offerings to create more relevant, efficient, and satisfying customer experiences.

Data security

Data security refers to the measures and practices used to protect an organisation’s data, like user permissions and role-based access, to ensure only authorised individuals have access to specific data.

• What it means for customers: Customer trust is everything. When customers know that an organisation takes great care in protecting their data and privacy, it builds relationships and loyalty.
• What it means for teams: When teams have data security measures in place, they protect themselves from data breaches, maintain customer trust and reputation, ensure they comply with regulatory standards, and even safeguard intellectual property.

Data storytelling

Data storytelling is the use of data, visualisations, and narratives to communicate insights and convey a compelling story to an audience. You can create stories to tell a data narrative, provide context, demonstrate how decisions relate to outcomes, or simply make a compelling case.

• What it means for customers: organisations use data storytelling to create a deeper, more meaningful understanding of their customers.
• What it means for teams: Teams use data storytelling to simplify complex information, and share it in an engaging way across their organisation. This improves understanding and buy-in of key data concepts and related projects.

Data visualisation

Data visualisation is the practice of creating detailed charts, graphs, and maps to make information easier to understand. This helps organisations better spot trends and patterns in data, and allows nontechnical people to understand and make sense of data.

• What it means for customers: Customers have more connected interactions with a brand when organisations are on the same page about data insights.
• What it means for teams: Teams enrich their understanding of data and uncover hidden insights with rich visualisations.

Data warehouse

A data warehouse is a large, organised storage space for processed data, where an organisation collects and stores information from different sources in a structured way.

• What it means for customers: Customers expect their interactions with a brand to be seamless. organisations meet this expectation better when they have all their data organised in a single place.
• What it means for teams: Teams have a central hub for data, which gives them quick access whenever they need it for reporting, decision-making, and more.

Predictive analytics

Predictive analytics uses statistical techniques (including machine learning) to predict future events or outcomes based on historical data. In the context of CRM, this might involve predicting which customers are most likely to churn, or which are most likely to respond to a certain promotion.

• What it means for customers: With predictive analytics, customers receive the information and promotions that are most interesting and relevant to them.
• What it means for teams: Teams can use predictive analytics to forecast demand, identify trends, make proactive decisions, and inform business strategies.

Structured, unstructured, and semi-structured data

Structured data is well-defined data in a fixed format, such as a spreadsheet or customer database, with rows for each customer and columns for name, address, phone number, and email. Structured data is easily understandable, searchable, and machine-readable by traditional analytics tools.

Unstructured data is information that doesn’t have a predefined format or specific data model, and requires specialised tools to create insights. Examples of unstructured data include emails, social media posts, audio and video recordings, images, and web pages. Because unstructured data is growing at a higher rate than structured data, big data technologies that can seamlessly analyse it will be crucial to businesses.

Semi-structured data has some organisational structure but isn’t easy to analyse as-is; it needs some organising or cleaning to be imported into a relational database like structured data.

• What it means for customers: Brands that take advantage of different data types can better serve their customers by deriving insights from more quantitative structured data and more qualitative unstructured data.
• What it means for teams: Teams can use all three data types for analysis, with a combination of solutions like Hadoop for ingesting unstructured data, and Tableau for analysing and visualising structured and semi-structured data.

Take the next step with data

Data is more important now than ever, and the ever-expanding flow of data is a huge management and governance responsibility. But data holds great power. The more you expand data access and data literacy for individuals throughout your organisation, the greater the potential for business insights that can guide decision-making and create incredible customer experiences. When you combine real-time, actionable data with AI and CRM, it can drive intelligent actions and deliver personalised experiences at scale.

That’s why it’s important to understand the data essentials. When data literacy spreads throughout your company culture, anyone can gain insight with data and create value.

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The post This New Data Glossary Will Demystify Data for You and Your Teams appeared first on Salesforce.

I’ve been helping people build better charts for 15 years, through my book, The Big Book of Dashboards, on Chart Chat (a video series about the good and bad of data visualisation), and at conferences around the world. I’m a senior data evangelist at Salesforce’s analytics platform Tableau, and here’s what I know: data visualisation is a language as expressive as the written word, a language that needs to be understood by all but often is not.

I’m endlessly fascinated by the power of data to persuade or inform. Seemingly trivial choices such as colour or orientation can completely transform a chart’s message. Check out this two-minute video for a perfect example of how a simple bar chart can be manipulated to tell two completely different stories.

In this post, I’ll share my top tips that will help you improve any chart. Each one represents a core aspect of data literacy skills. This list could also form a checklist you can use when creating charts to ensure you’ve made something that will work for your audience and help you communicate your message more efficiently. I’ll show the tips by making incremental changes to a typical chart you might find on any dashboard. Below is our starting point, an everyday pie chart:

Tip #1 – Avoid the pies!

Pie charts are everywhere. Humans love circles, but pie charts are rarely useful. Consider our example above. Who has the biggest sales? It’s hard to answer the question. Let’s apply our first incremental improvement and switch to a bar chart:

It’s not only easier when the data is shown in bars, your brain efficiently processes the bar chart on the right before you even consciously think about the data. 

How can that be? Data visualisation takes advantage of “preattentive attributes.” These are environmental signals that we process subconsciously, and they’re super-useful. Length, colour, size, and angle are all examples of preattentive attributes. However, we process some more efficiently than others. Our brain is terrible at efficiently comparing sizes of slices in pies, but wonderfully efficient at perceiving even the smallest differences in lengths. 

A chart should deliver its message in the most accurate way possible. Choosing the right preattentive attribute is a critical skill. Don’t just click the button that makes the most “attractive” chart. You should always think about whether the chart actually conveys the message you want it to.

Tip #2 – Avoid distractions (aka, “don’t make me tilt my head!”)

Your goal when making charts is to reduce the cognitive load on the audience as much as possible. How many times have you seen a chart with the labels rotated vertically? I see it all the time, most often on bar charts. Why not simply rotate the chart so the bar is horizontal?

Making these incremental changes helps people get to the insight more quickly because they can focus on data, not formatting. Other distractions that often clutter charts are excessive gridlines, intrusive axes or borders, or the use of three dimensional charts.

Tip #3 – Use colour with intent

Avoid the temptation to use too many colours in your visualisations. In all analytics software, colours are just a click away, and it’s easy to feel productive by sprinkling some into your charts. 

Take a step back. Is the purpose of your analytics to make pretty rainbows, or to share insights?

Some of the most powerful and effective charts use only one colour. In the below example, the multi colours used on the left serve no purpose. Readers might be confused as to what the different colours even signify. The example on the right is more effective in highlighting the intent, which is identifying the top two sellers.

Tip #4 – Choose a good title

After choosing an appropriate chart, orienting it correctly, and using colour to highlight, you need to stop and think: what is this chart actually showing? What conclusion do I want people to draw from this chart? 

The first thing people look at on your chart, and the most likely thing they’ll remember, is the title. It’s the one chance you get to tell them what they’re going to see and suggest the insight you want them to take away. A good title should describe the insight the chart shows — this could be in the form of a short phrase or question. Getting in the habit of making good titles also forces you to be sure you know why you are making the chart in the first place. 

In this  example, I’ve created an effective 2-level title on the right. This allows a clear intro (“Who are our top 2 sellers?”) with a sub-heading for added context.

Tip #5 – Show the right number

My final tip is possibly the one you should consider first. Are you even showing the correct number to your audience? So far, we’ve been looking at sales by Account Executive. It looks like Christine and Andrea are our leaders. However, sales success is measured against a quota, not the actual sales value themselves. Have we been charting the wrong number all this time? Let’s take a look at the data:

We can see Christine and Andrea ranking top, but look across to the target and percent of target values: they’re behind their quota! Perhaps, then, we’ve been visualising the wrong value all along. Let’s take a look at the account execs ranked by percent of target. It turns out that Dan, Allie, and Wanda are the superstars who are above quota. Dan’s way ahead, even though his actual sales value is the lowest in the team.

These tips will help improve your charts, but they are just the starting point. You can accelerate your learning by joining thousands of other data-inspired people in our Tableau Community.

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This post originally appeared on the U.S.-version of the Salesforce blog.

*Source: 2022 Salesforce Success Metrics Global Highlights study. Data is from a survey of 3,706 Salesforce customers across the US, Canada, the UK, Germany, France, Australia, India, Singapore, Japan and Brazil conducted between June 8 and June 21, 2022. Results were aggregated to determine average perceived customer value from the use of Salesforce. Respondents were sourced and verified through a third-party B2B panel. Sample sizes may vary across metrics.  

The post No One Understands Your Charts — Here’s How To Change That appeared first on Salesforce.