Analytics & Business

The sheer volume of data available is staggering, presenting both opportunities and challenges. Our ability to harness and interpret this data can have a direct impact on the direction and success of businesses.

There is increasing pressure on businesses to not only adapt to this new world of AI and data but to thrive. Right now, there is huge potential for organizations to gain a competitive edge through effective data analytics.

The goal of this blog post is to demonstrate the link between data analytics and the decision-making process. We’ll focus on a framework for going from analysis to decision, as well as some practical applications used by tech giants so that you can find ways of applying them to your own business.

The Four Stages of Analytics

In the book Competing on Analytics by Thomas H. Davenport and Jeanne G. Harris, four stages of analytics are given that serve as a guide for approaching data-related problems and make it much easier to select the most appropriate solution.

These stages, in order, are:

1. Descriptive analytics — made up of standard and ad hoc reporting of historical data, providing a snapshot of what happened in the past. At this stage, you look into the past to determine where the problem is and what actions are needed.
2. Predictive analytics — these are statistical analyses, forecasting, and predictive modeling. Here, you’re analyzing historical data and trends and asking why and what’s next if the data follows the same pattern.
3. Prescriptive analytics — consisting of experimentation and optimization. At this stage, you run experiments and find the best possible scenario.
4. Autonomous analytics — machine learning and beyond. From this point onwards, you are trying to reduce the amount of human involvement, building models that learn from the data on their own.

Going From Analysis to Decisions

Regardless of which stage of analytics your company is in, the goal of analytics is to support the decision-making process so that companies can achieve a specific strategic objective or solve a problem. There are two main aspects to going from analysis to decision:

1. A clear data analytics strategy
2. A solid decision support system

Data Analytics Strategy

The first step in ensuring your data projects successfully go from analysis to decision is with a clear data analytics strategy.

A data strategy starts with understanding the goal, identifying the problem, and then choosing the best solution. What are the elements of a good data strategy?

• An outline of the company’s current strategic objectives along with the problems analytics could solve or the decisions that analytics can support.
• An indication of the best analytical solutions for these problems or support areas.
• An assessment of the current and future data sources in the context of how the data will be used, analyzed, and reported on.
• An evaluation of current infrastructure and whether further development is required.
• An assessment of the analytical talent within the organization and whether there is a need to hire more talent.

If you want to learn more about creating a good data strategy, I wrote a step-by-step blog post on how to do just that.

Finally, analytics projects must be interpreted, explained, and clearly communicated to stakeholders. This goes beyond technical concepts, algorithms, and code. You or your analytics team need to be able to tell a compelling data story that provides actionable intelligence.

Decision Support Systems

It is common to find a large gap between the decision-makers in an organization and the data scientists and analysts. This is where decision support systems are crucial. We can draw on the field of decision intelligence to figure out how to bridge this gap.

Decision intelligence requires a new way of thinking for both decision-makers and analysts. By focusing on decisions first and allowing them to be the driver of everything else, we can do a much better job of working together. Ultimately, models, reports, and analyses are not of any value (no matter how complex or cutting edge they may be) unless they lead to some action.

Leveraging decision intelligence to build a decision support system usually involves two broad steps:

1. Develop a Causal Decision Diagram (CDD) to evaluate the decision levers that lead to a certain outcome.
2. Create an interactive decision model that visually represents how data and analytical solutions can objectively determine the effect of a decision.

You can read my introduction to decision intelligence for more information.

Practical Applications for Businesses

Netflix

If you use Netflix, you will know that your watchlist is curated with precision. Netflix utilizes analytics to understand viewer preferences, delivering a personalized streaming experience. This customization isn’t limited to content suggestions; it extends to optimizing user interfaces and enhancing overall user satisfaction.

They collect data such as the time of day that users are streaming shows, whether or not a show was binge-watched or watched over a longer period of time, and other viewing behaviors such as pausing, leaving, and never coming back to a show, and so on.

Amazon

Amazon, the e-commerce giant, relies heavily on analytics to optimize its operations. There are two stand-out ways that Amazon showcases its analytical prowess.

First is their dynamic pricing algorithm. Amazon changes its prices many times daily based on customer shopping patterns and behaviors on its site, competitors’ prices, and historical sales data for a product.

Second, their product recommendations. Amazon has developed algorithms that deliver product recommendations based on purchases, clicks, views, and cart contents. This allows them to learn, with amazing accuracy, what their customers want or like.

Across industries: the adaptability of analytics

The beauty of analytics solutions lies in their versatility. Whether it’s healthcare, finance, or retail, the principles of analytics can be tailored to suit the unique needs of each industry, dataset, or business problem.

Conclusion

In business, the impact of data analytics on decision-making is nothing short of transformative. However, it is not always clear or obvious how we can go from analysis to decision.

In this blog post, we discussed the four stages of analytics, how you can develop a data strategy and decision support system to go from analysis to decision, and some of the practical applications, using the example of Netflix and Amazon to demonstrate how analytics can be used for customization and optimization.

I hope you found this helpful! If you have any questions or comments, drop them below. I’d love to hear from you!

You can also connect with me on LinkedIn where I post more data science content.

Approaching data science like a team sport is still a relatively new concept in the field today. In the past, data scientists have typically worked alone as ‘unicorns’ and have been expected to wear multiple hats. However, more and more companies are building teams that contain a mix of roles from BI experts, data analysts, data scientists and data engineers, all the way up to Chief Data Officer.

Effective collaboration and communication are critical to the success of data science projects, not just within the data science team but across other teams too. This means building cross-functional teams that work together toward a common goal.

In a world that is also becoming increasingly remote, companies have been trying to find more effective ways of collaborating on projects across almost all business units. Deepnote is working to solve this problem for data scientists with their cloud-based notebook platform. Deepnote was built with data teams in mind, taking the headache out of many of the unique challenges that these teams usually face.

These are the three biggest challenges that small teams face when working collaboratively on data science projects:

• Working together in real-time — being able to see the code and output in real-time, where ideas can be shared and feedback can be given in the moment.
• Building reproducible data science projects that can be shared with new and future team members quickly and with little effort.
• Going from data science to data storytelling – giving presentations on the results of data science projects in the form of a story to leadership and executive teams in the company.

In this post, we’re going to expand on these issues and demonstrate how Deepnote has addressed them.

Working Together in Real-Time

When working in a remote environment, it’s easy to lose sight of the importance of collaboration. You can’t see what a team member is doing or what they’re working on unless you get on a Zoom call and share your screen or take a series of screenshots and send them over Slack or email. This adds friction and is definitely not the most efficient way to collaborate. It slows down your work, and projects end up taking longer or get scrapped altogether.

Being able to write code alongside teammates allows you to each get involved in the project, sharing ideas and feedback in the moment. You can bounce ideas off of each other and explore different ways of approaching a problem. By sharing knowledge, team members can learn faster and find better solutions to business problems.

This is particularly useful when you’re stuck on a challenging problem or get lost down a rabbit hole that takes you in the wrong direction. Being able to interact with one another and demonstrate, in real-time, what you’re doing is an element of collaboration that is missing from a lot of the current processes and tools that data teams use.

Deepnote reduces the friction, allowing teams to easily work together in real-time. You just share your notebook with team members and their avatar will show up on the screen. Clicking on their avatar allows you to quickly jump to their position in the notebook to avoid excessive scrolling (very useful for large notebooks).

Deepnote also gives you the ability to take your communications offline by leaving comments on specific code cells. This allows teams to have discussions and share knowledge and ideas even if real-time collaboration is not possible (e.g., when working in different time zones).

Building Reproducible Projects

Reproducibility is a key prerequisite for reliable science. Since the package versions, environment, and computational requirements are not bundled with Jupyter notebooks, reproducing a colleague’s analysis can be frustrating.

On the other hand, if you’ve ever tried onboarding a new team member, you’ll know how time consuming it can be to get their environments set up with the correct software and packages so that they can work on current projects and also be able to refer back to past ones.

This is why reproducibility matters — it will save you time in the long run and you can produce projects that are more accurate, more efficient, and with fewer bugs.

There are 3 main areas of a project that need to be reproducible: code, environment, and compute. With Deepnote, you can build projects that are reproducible in all 3 of these areas and you don’t even have to really think about it.

Every project on Deepnote comes with pre-installed packages that are commonly used in data science projects and everything just works. However, you do still get a lot of flexibility to set up your own custom environments if you need it. 

Bonus tip: you’re not even restricted to just using Python if that’s not your thing — you can choose a different kernel to run on Deepnote, such as R or Julia. You can also directly insert SQL blocks for querying your data so that everything stays in one place.

Deepnote also offers a choice on the type of machine you want to use for your project. However, for the majority of data science projects, their Standard machines offer more than enough compute. The benefit here is that once you select a machine for a project, you never have to worry about it again. There is no maintenance or admin required and you can easily start or return to a project in seconds.

Lastly, there’s the code. Deepnote comes with automatic version histories and snapshots of files. This makes it easy to keep track of the changes that collaborators made to the project and roll back to previous versions.

A good philosophy to follow is to design notebooks that can be read, run and reused.

From Data Science to Data Storytelling

Data storytelling is one of the most overlooked skills in data science. While having the technical skills needed to properly analyze data is incredibly important, it doesn’t mean anything unless you’re also able to communicate the insights and results. Without this piece of the puzzle, no one will understand or much less, use your model.

Effective data storytelling allows the insights and results of data science projects to inspire people to take action. This moves the company toward its strategic goals and highlights the valuable contribution of data teams.

One way to communicate the results of a project is with a static presentation. You take a bunch of screenshots of your results, create a few charts and stick it all in a PowerPoint presentation and hope for the best. While this may be one of the most common ways to prepare presentations, it is not a good way to tell a compelling data story.

The most effective form of data storytelling is a combination of good visuals and interactivity. Luckily, Deepnote allows you to do both! Using their Publishing Editor, you can transform your analyses into either an article with a more readable layout or a dashboard with interactive blocks that can be moved around and resized. In other words, you can turn your notebooks into apps!

Closing Remarks

Improving our ability to collaborate within and across data teams is a crucial factor in the success of data science projects. While data teams do face some unique challenges, there are ways to overcome them.

With Deepnote, you can collaborate in real-time, create reproducible projects, and create interactive web apps from notebooks — all in the same platform.

Click here to start building your data science projects on Deepnote!

This is a sponsored blog post, but all opinions are my own.

Business Intelligence forms the back-bone of decision making in an ever-growing number of organisations in the world today. More than ever, organisations need to be able to make fast decisions to changes in the market.

Unfortunately, many businesses do not have a system that allows them to obtain the insights they need to do this. Instead, they rely on spreadsheet software like Microsoft Excel to capture, manage and analyse all their business data.

Let’s face it, spreadsheets are one of the most accessible, cost effective and flexible solutions to use when your business is in it’s early days and you don’t really have much data to work with.

However, every business reaches a tipping point as they grow when there’s just too many moving parts to get a proper handle on things and decision making end up suffering for it. The spreadsheet you used to use to track sales, marketing, customers, and inventory is now starting to gain a few holes. Preparing and maintaining the spreadsheet is probably taking up more of your time than you’d care to admit, you can’t identify trends and connections between all the various areas you’re tracking and the data just isn’t as accurate as you’d like.

Long story short, you may have reached a point where you need to level up your reporting and analytics capability and a business intelligence system will get you there.

What is Business Intelligence?

BI is a catch-all term for a range of technology and software that enables organisations to manage and analyse data. Broadly speaking, BI is about combining data from multiple sources and using it to make better decisions.

Over the years, BI tools have evolved into being more of a self-service solution. This makes it possible for anyone in the organisation to analyse data and create their own reports and dashboards. Gone are the days when you had complex models that were built and understood only by the specialist who created them. The BI tools of today are more accessible, intuitive and easier to understand.

There are many BI tools and software out there, some of which are free and some come with a hefty price tag. However, business intelligence is about more than just the software. BI involves data storage, data governance, reporting, insights, and also promotes a shift in the culture of the organisation towards data-driven decision making.

Benefits of Business Intelligence

Faster & more accruate reporting and analysis

BI tools are designed to handle large amounts of data from multiple sources. All of this happens behind the scenes so that you only need to focus on extracting insights. The time and energy saved on this front will allow you to prioritise the things that really matter in your business.

Examples of the kinds of reporting and analytics that BI platforms are capable of can be found throughout the organisation. From product development and greater operational efficiency to improving customer experience and employee satisfaction.

With this kind of insight available to business leaders, it is easier to innovate and find new opportunities.

Single source of truth

BI platforms combine data from various internal and external sources into a single warehouse that is more accessible.

This provides a single source of truth for the data where data governance and maintenance is much easier. Not to mention, you avoid inaccurate spreadsheets or databases that are siloed within a single department. With consistent, higher-quality data available, employees are more likely to use and trust the reports and dashboards that are generated.

Establishing a data-driven culture

Building a business intelligence system leads the organisation down the path of developing a data-driven culture. Decisions are based on data and experimentation rather than gut and guess-work. A data-driven culture is essential if the organisation wants to ensure long-term adoption and benefit of current and future systems.

This is a journey, but it is a worthwhile one. Organisations with a data-driven culture are committed to developing their analytical capability, finding insights and deriving value from their data. This approach leads to greater collaboration within and between departments and a mindset of curiosity and discovery.

Business Intelligence Trends in 2021

A recent survey with over 2,200 participants shed some light on the data, BI and analytics trends seen in 2021. These are the top 5:

• Master data and data quality management
• Data discovery and visualisation
• Establishing a data-driven culture
• Data governance
• Self-service analytics

Essentially, these trends make up the foundation of a good business intelligence system. Organisations must be able to obtain, govern and use their data towards making better decisions. Ultimately, this will help the organisation to move towards their strategic objectives.

BI Is Definitely Worth It

I hope this post helped you to decide whether you want to build a BI system in your organisation. In my opinion, BI is definitely a worthwhile investment and it is easier than ever to get started. Power BI is one of the leading BI platforms on the market at the moment. It is also free to download and start using on your PC immediately.

If you do decide to build a business intelligence system in your organisation then I recommend that you develop an analytics strategy that will also serve as your roadmap. Start small, experiment and make improvements as you go.

For more information check out these resources:

• The Best Self-Service BI Tools, PCMag
• Benefits of Business Intelligence – 2,500 Projects Analyzed, BI-Survey

What are your views on BI? Let me know in the comments below!

Those companies that view data as a strategic asset and develop robust data and analytics strategies are the ones that will succeed in this new data-driven world.

Bernard Marr

It is very easy to get caught up in the latest buzzword, jumping on the big data bandwagon without any clear direction on where you would like all that data to take you and how it can actually benefit your organisation.

Analytics is not something you do just so that you can have the bragging rights to say “yeah, we do all the big learning, deep data analytics stuff”. It is a journey your organisation goes on – analytics is something you nurture and develop within your organisation so that you can eventually cultivate an environment that supports better data-driven decision making and thus promotes a sense of curiosity in the organisation that is also backed up by facts and data.

By going through the following 5 steps in this blog post, you will be able to put together an analytics strategy that identifies the focus of your analytics initiatives, understand what data, analytical talent and infrastructure is required to carry out those initiatives, as well as the vision of what success looks like and exactly how analytics will benefit your organisation.

Think of each step like a roadmap: start with the lowest-hanging fruit and then rank each potential project along the roadmap according to the complexity of the requirements. Eventually you will have laid out the journey in front of you showing the progression of analytics within your organisation.

It is also worth noting that an organisation does not have 1 single analytics strategy that lasts throughout the lifetime of the organisation. An analytics strategy will follow the strategic objectives of the organisation and will change as the strategic objectives change.

Step 1: Strategic Objectives

A good data strategy is not about what data is readily or potentially available – it’s about what your business wants to achieve, and how data can help you get there

Bernard Marr

When putting together a data analytics strategy, the organisation first needs to identify its own strategic objectives for the business and then it can determine how data and analytics can help them achieve those objectives.

When setting a focus for their first or next analytical project, an organisation can ask themselves:

• What is our current strategic objective?
• What information or insights do I need to achieve this strategic objective?
• What key decisions within the strategic objective need support from analytics?

For example, the key business objective for quarter one could be to increase market share. The organisation then needs to break down the various decisions that need to be made to achieve this objective and what kinds of information might be needed to make those decisions. From this they can determine one or more focal areas to target their analytical efforts.

Doing this will ensure that the analytics project is highly relevant to the current needs of the organisation and will improve the chances of success and will maximise the value that analytics can add.

For organisations that are that have never implemented analytics before in their organisation, there is often a need to first prove the value of analytics before any investments are made by the organisation. This can be very tricky and almost entirely depends on the amount of senior-level support there is for analytics. According to the book Competing on Analytics by Thomas H. Davenport, when there is analytical support at the highest level of the organisation then analytics will advance rapidly. However, when the organisation must take the ‘prove-it’ approach then these are the steps they can follow:

1. Find a sponsor and a business problem that can benefit from analytics
2. Implement a small, localised project to add value and produce measurable benefits
3. Document the benefits and share the news with key stakeholders
4. Continue to build localised successes until the organisation has acquired enough experience and sponsorship to develop an organisation-wide analytics strategy that directly supports the strategic objectives of the organisation

Once the organisation has identified a focus for their analytics project (and have some senior-level support), they now need to assess what the organisation’s current capability is for analytics: that is what data they already have, whether they have analytical talent and what IT infrastructure they possess. It’s no good investing in high quality data or upgrading the IT infrastructure if the organisation does not have anyone with the skills to analyse the data and carry out the project to completion.

Each of these areas must at least have some capability and if not then there should be a plan for how to improve that area before beginning an analytics project. This does not mean that the organisation must first have the best-in-class talent or data or infrastructure before starting their project – in most cases that requirements are much lower and it is far easier to get started than you may think.

Step 2: Data

This step involves questions such as:

• What data do we need?
• How will we gather the data?
• How will the data be analysed?
• What level of detail is required in the data?
• How will we report and present insights?

Remember that not all business problems are solved with machine learning and AI – very often all that is needed initially are a few key insights and as the organisations analytical capabilities improve then more advanced statistical modelling can be explored.

Thus analytical initiatives can be ranked according to their complexity and time requirement. You may start with the relatively simple, quick analyses that benefit the organisation in the shortest time possible and thereafter larger, more complex projects can be carried out.

Step 3: Analytical Talent

In this step, you will need to determine who you can hire or train to advance the chosen analytical initiative for the lowest-hanging fruit. You can determine the type of skills your employees require if you know exactly what is required for the project (i.e. thoroughly mapping out the requirements from step 1 and 2)

Answer these questions as a guide:

• What are the current data competencies within the organisation?
• Do we need to hire more analytical talent?
• Do they need to have knowledge of specific software?
• Do they need to have knowledge of specific analytical techniques?

Step 4: Infrastructure

In this step you will need to determine:

• What are the technology requirements of each project along the roadmap?
• How should our technology be developed to support each analytical initiative and can we upgrade the technology in a similar time frame?

Initially, your infrastructure requirements may be very low for projects that involve obtaining just a few key insights – typically all that is needed is spreadsheet software and a some good questions that need answers. However, as you progress along your roadmap, you will inevitably need more complex infrastructure that can support your analytics on a consistent, ongoing basis.

Step 5: Definition of Success

For each analytics project that the organisation undertakes, there should always be a clear definition or vision of what success looks like at the end of the project and exactly how the project can benefit the organisation.

Ask yourself what the project’s key performance metrics are that can be monitored throughout the project.

Having this last step in place will prevent analysis-paralysis, causing a project to take much longer than it needs to. This could also allow the analytics team or management to make a decision early on that the project is a failure and quickly change course and focus their efforts on something more productive and valuable.

References / Further Reading

If you want to go deeper on the topic of analytics strategy then I recommend these books:

1. Competing on Analytics: The New Science of Winning
2. Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things

References for this post:

1. Why Every Business Needs a Data and Analytics Strategy, Bernard Marr
2. Six Steps to a Business-Driven Analytics Strategy, SAS
3. Competing on Analytics: The New Science of Winning

Comment below and if you have any questions or let me know what your experience has been with creating an analytics strategy in your organisation.

Power BI has been my go-to data visualisation and business intelligence tool for the last 3 years. I have built corporate dashboards with it and used it as my own little exploration tool when I’m tackling a new data set for the first time.

Since I’ve been getting more and more proficient at using the tool, there are some functions that I go back to time and time again. I had a look through the reports and analyses that I conducted in 2020 and picked out my most used DAX functions – I found 17 functions in total. Read on to learn more about them.

The below functions are mostly in order of usage with the first category being the most used and the last category being the least used.

All generalised syntax below has been taken from the official Power BI reference documentation.

1. BASIC SUMMARY FUNCTIONS

This is the only category of functions I have in this list containing 7 of the basic summary functions. These are probably going to be the first functions anybody learns when just getting started with Power BI and they are definitely going to make the list for the most common functions used by just about anybody.

I would usually use any one of these functions together with the other functions coming up next. In Power BI, it isn’t always necessary to create measures just to perform a simple SUM or an AVERAGE, for example because these operations can be performed within each chart or table without needing to go through the extra effort of creating a measure.

The only time I might use these functions on their own is to give the measure a more meaningful name and this allows me to:

• Preserve the original name of the column
• Not have to rename the chart field or table column each time I add the column to a chart/table
• Get around the (often ugly-looking) automatic naming of fields when applying an aggregation to a field/column in a chart/table

COUNT & DISTINCTCOUNT

The COUNT function counts the number of non-blank cells in a column.

COUNT(<column>)
DISTINCTCOUNT(<column>)

And obviously DISTINCTCOUNT just counts the number of unique values.

SUM & AVERAGE

These functions allow you to SUM or AVERAGE the values in a column

SUM(<column>)
AVERAGE(<column>)

MIN & MAX

These functions return the MIN or MAX values of a column

MIN(<column>)
MAX(<column>)

2. SUMMARIZE

Alright, now we get to the somewhat more interesting functions.

I probably use the SUMMARIZE function the most and is my favourite (if I can actually pick a favourite).

Unlike the other functions in this list (except for LASTDATE at the end), the SUMMARIZE function returns a table and I typically use it so that I can further apply aggregations over different groupings.

For example, suppose my main table is highly transactional and I need to roll it up a bit so that I can create new functions that aggregate on the new rolled up table. The SUMMARIZE function will take in grouping variables and named expressions and return a new table from it.

You can also join on these tables and create relationships just as you would on a normal table.

Pretty nifty if you ask me.

SUMMARIZE (<table>, <groupBy_columnName>[, <groupBy_columnName>]…[, <name>, <expression>]…)

Here, square brackets indicate optional arguments and the ... portion simply implies that more arguments can be added.

3. CALCULATE

The CALCULATE function performs an aggregation along with one or more filters. When you specify more than one filter, the function will perform the calculation where all filters are true.

The general syntax is as follows:

CALCULATE(<expression>[, <filter1> [, <filter2> [, …]]])

The CALCULATE function can be useful to you force an aggregation to completely ignore some or all report-based filters. However, in the reports I have designed, I have only used this a handful of times and only for very specific use cases.

4. IF

Ah, yes. The great and powerful IF function.

I use the IF function A LOT. But mostly when creating a new flag or classification column and then further combining it with the other calculation functions listed here.

Also feel free to nest the IF function within other IF functions as much as you like.

IF(<logical_test>, <value_if_true>[, <value_if_false>])

5. IFERROR

Very similar to the IF function above but specifically looks for any errors and returns an alternative result.

This function is a life saver when trying to catch errors and display them in a more aesthetically pleasing way.

Also works well with the BLANK function which returns a blank value that is readable by Power BI (because using empty quotes like ” ” may not be regarded as a blank and is important to note when you are filtering tables/charts).

IFERROR(value, value_if_error)

6. ISBLANK

Used a lot in conjunction with IF to create flags that highlight blanks or to exclude them altogether.

ISBLANK(<value>)

7. WEEKDAY

I use this function to create a week start date or week end date in my date table. The return_type argument is very important because it determines what day of the week the week begins on.

WEEKDAY(<date>, <return_type>)

This is the week start date formula:

Week Start Date = date - WEEKDAY(date, 2) + 1
Week End Date = date - WEEKDAY(date, 2) + 7

This formula for week start date is the most versatile I have ever used and handles new years perfectly.

8. FORMAT

This function can be used to convert columns to any format. However, I use this function to create a month-year or period column from the date column in my date table.

FORMAT(<value>, <format_string>)

This is the formula for creating a month year column:

MonthYear = FORMAT(date, "MMM YYYY)

Using the FORMAT function in this way will force the resulting MonthYear column to be converted to text. However, there is a little trick for displaying this as text but allowing the correct month sorting (rather than alphabetical sorting). Leave a comment below if you’d like me to go over this little hack.

There are many use cases for when you want to force Power BI to display dates in a very specific way rather than allowing it to decide how best to present your dates so don’t discount the power of using this function in this way.

9. LOOKUPVALUE

If you are familiar with VLOOKUP in Excel then you will be comfortable with LOOKUPVALUE too.

This function allows you obtain a single column from another table by creating a link that exists in this function only rather than having to completely link the two tables together.

However, as stated in the Power BI documentation:

If there is a relationship between the result and search tables, in most cases, using RELATED function instead of LOOKUPVALUE is more efficient and provides better performance.

LOOKUPVALUE(
    <result_columnName>,
    <search_columnName>,
    <search_value>
    [, <search2_columnName>, <search2_value>]…
    [, <alternateResult>]
)

10. EOMONTH

Used to get the last day of the month of a given date for as many months in the past or the future.

This function can be useful when used together with the CALCULATE function as a filter argument.

EOMONTH(<start_date>, <months>)

11. DATEDIFF

Used to get the difference between 2 dates in days, months, quarters, years, etc.

DATEDIFF(<start_date>, <end_date>, <interval>)

You know, even after using this function so many times I always forget which way the calculation is performed and I have to try both ways. Is it start_date – end_date or is it end_date – start_date?

42?

12. LASTDATE

This function returns the last date in a column.

This function is a little sneaky in that it actually returns a table with one column and one row and so you can use it in any function that requires a table argument.

LASTDATE(<dates>)

I hope you find this list of functions useful to you. Once you learn a couple of the basic functions in Power BI and then expand to include a few common functions you can solve almost any problem and design powerful reports and dashboards!

Happy visualising!