Service Category: Management

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Getting started with data & analytics as a small business

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Starting a small business can be challenging, but with data and analytics, you can make data-driven decisions that will help your business grow. But where do you begin? The idea of data and analytics can be overwhelming, but with the right guidance, it can be a valuable tool for any small business.

Step 1: Identify Your Goals

The first step in getting started with data and analytics is to identify your goals. What do you hope to achieve by using data and analytics? Some common goals include:

  • Improving customer retention
  • Increasing sales
  • Reducing costs
  • Identifying new business opportunities
  • Understanding productivity and efficiency

Once you know what you hope to achieve, you can start to think about how data and analytics can help you reach those goals.

Step 2: Collect and Organise Your Data

The next step in getting started with data and analytics is to collect and organise your data. This may include sales data, customer information, and website analytics. It’s important to ensure that your data is clean, accurate, and up-to-date – things that a data governance approach will address.

To collect your data, you may need to set up tracking systems such as Google Analytics to track website traffic and behaviour. You can also use tools such as a CRM (customer relationship management) system to collect customer information and sales data. Tools like ERP (enterprise resource management) systems manage information on your operations and inventory levels.

Once you have collected your data, it is important to organise it in a way that makes it easy to analyse. With multiple systems, this may include creating a data warehouse or data lake, where you can store and organise all of your data in one place. You may also need to perform data cleaning to ensure that your data is accurate and consistent. Custom data warehouses directly address your need, but turnkey solutions exist for many popular platforms that can provide a good starting point while you up-skill.

Step 3: Analyse Your Data

Now that you have your data organised, you can start to analyse it. This may include looking at sales trends, customer demographics, and website analytics. You can use tools ranging from Excel, Google Analytics, and Amplitude, right through to custom Python code to help you with this step.

When analysing your data, it’s important to keep your goals in mind. For example, if your goal is to increase sales, you may want to focus on analysing sales data and identifying trends or patterns. If your goal is to improve customer retention, you may want to analyse customer data and identify factors that lead to customer churn.

There are a variety of tools and techniques that you can use to analyse your data, such as descriptive statistics, data visualisation, and machine learning. Data analyst specialists – like my team at Vaxa Analytics – can help you with this step and provide you with more advanced analytics techniques like predictive modelling, A/B testing, and customer segmentation.

Step 4: Use Your Data to Make Decisions

Once you have analysed your data, you can use it to make decisions. This may include changes to your marketing strategy, changes to your products or services, buying new equipment, or even hiring new staff.

While these steps may seem simple, they can be challenging to implement on your own while running your core operations. Consider the balance of implementing these steps yourself versus engaging help; we help many small businesses who’d rather focus on their operations to take their first steps and extract value from their data.

In conclusion, data and analytics can be valuable tools for small businesses, but it can be overwhelming to get started. By identifying your goals, collecting and organising your data, analysing it and using the insights to make decisions, a small business can gain a competitive edge. And when the going gets tough, consider engaging an analytics partner to guide you through the process and help you make the most out of your data.

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SMEs and data governance: an approachable guide

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Small-medium enterprises are constantly looking for ways to improve their business operations and make data-driven decisions. However, many SMEs struggle with understanding and implementing effective data governance practices – the very practices which underpin all data-driven decisions. In this beginner’s guide, we’ll take a look at what data governance is, why it’s important for SMEs and some best practices for getting started.

What is data governance?

Data governance is the set of policies, procedures, and standards that govern how data is collected, stored and used within an organisation. It ensures that data is accurate, consistent, and compliant with regulations and industry standards. This includes the management of data quality, data security, data privacy, and data lineage.

At Vaxa Analytics, we group data governance under our Data Management offering which, together with Data Strategy and Data Analysis, form the foundations for extracting actionable insights from your data and helping you answer the questions you didn’t even know you should be asking.

Why is data governance important, even for SMEs?

Data governance is essential for SMEs to make sure that their data is accurate, consistent, and compliant with regulations and industry standards. Without effective data governance, SMEs risk making decisions based on inaccurate or unreliable data, which can lead to costly mistakes and negatively impact their bottom line. Additionally, with increasing regulation on privacy and data management, it’s more important than ever for SMEs to have a strong data governance program in place.

Data governance doesn’t get any easy the larger an organisation gets. Indeed, it’s quite the opposite.

Setting solid foundations helps to avoid growing pains in the future. Thankfully, SMEs benefit from their smaller scale, meaning small changes have a relatively large impact on the organisation’s data governance maturity.

Getting started with data governance

An excellent first step in implementing data governance in a small-medium enterprise is to establish clear roles and responsibilities for data governance within the organisation. Assign specific individuals or teams to manage data governance and ensure that everyone within the organisation understands their roles and responsibilities. This is vital as it sets the foundation for effective data governance by ensuring that there is someone accountable for managing data governance within the organisation.

From there, the next logical step is to develop policies and procedures for data collection, storage, and use. This includes auditing your current policies (if any) alongside the data collected from the systems. From there, begin by crafting a data retention policy, data classification policy, data access policy, and data breach response plan. It’s important to physically document these policies (not just assume staff know what to do) because it establishes guidelines and rules for how data should be collected, stored, and used within the organisation, which will ensure that data is accurate, consistent, and compliant with regulations and industry standards. This step also ensures that everyone in the organisation understands and follows the policies and procedures, which will help to minimise the risk of data breaches and other data-related issues.

Now, let’s not kid ourselves – data governance has a wide scope and it isn’t a two-step process! Here are more activities that SMEs should consider — in rough order of priority (and roughly aligned to organisational scale):

  1. Regularly review and update policies: Review and update data governance policies and procedures regularly, such as annually or when regulations change, to ensure that they remain effective and compliant with current regulations and systems in use.
  2. Monitor and measure data quality: Implement processes such as data quality checks, data profiling, and data validation to monitor and measure data quality. Use data quality dashboards and reports to track data quality over time and take action to correct any issues that are identified.
  3. Ensure data security: Implement appropriate security measures such as encryption, access controls, and network security to protect sensitive data from unauthorised access and breaches.
  4. Educate employees: Provide regular training and education to employees on data governance policies, procedures, and best practices, such as data privacy and security awareness training.
  5. Consider getting help: If you’re struggling to implement data governance in your SME, consider engaging a consultant to assist. This is a specialist field, after all. Consultants can help you assess your current data governance practices, identify areas for improvement, and provide guidance on how to implement effective data governance.
  6. Implement automation: Use automation tools to automate data governance processes, such as data masking, data archiving, and data lineage tracking. This will help you to reduce the time spent on manual data governance tasks and increase the accuracy of your data governance.
  7. Implement Data Governance Platform: Implement a Data Governance Platform that will provide a centralised location to manage data governance policies and procedures, monitor data quality, and track compliance.
  8. Establish a Data Governance Council: Establish a Data Governance Council, comprising representatives from different parts of the business, to provide a common forum to discuss and resolve data-related issues and to make decisions on data governance policies. Note that this is more applicable to the larger end of the SME segment.

Cheat sheet for data governance terms

There’s a lot of terminology n the data governance space, and they’ll likely come up in any organisation’s data governance journey. Here’s a quick guide to the most common:

  • Data steward: A person or group responsible for managing and maintaining the quality and accuracy of a specific set of data within an organisation.
  • Data lineage: The ability to trace the origins, history, and movement of data through an organisation’s systems.
  • Data dictionary: A document or system that defines the different data elements and their meanings within an organisation.
  • Data quality: The degree to which data meets the needs of its intended users, is accurate, and is free from errors and inconsistencies, typically assessed under “the 7 data quality dimensions”.
  • Data security: Measures taken to protect data from unauthorised access, use, disclosure, disruption, modification, or destruction.
  • Data privacy: The protection of personal information and sensitive data, including compliance with regulations such as the EU General Data Protection Regulation (GDPR) and Australia’s Privacy Act.
  • Metadata: Data that describes other data, such as data element definitions, data lineage, and data quality rules. Metadata can help classify and categorise data, and therefore help make decisions on the appropriate rules and policies to put in place.

Data governance is essential for small-medium enterprises to make sure that their data is accurate, consistent, and compliant with regulations and industry standards. By following these best practices, SMEs can establish a strong data governance program that will help them make data-driven decisions and stay competitive in today’s business environment.

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Data migration

Your business generates vast amounts of data every day, but if that data is inaccurate, incomplete, or hard to access, it’s not doing you much good. That’s where data cleansing and migration comes in.

What is data cleansing and migration?

Data cleansing is the process of identifying and correcting errors and inconsistencies in your data, such as incorrect or outdated information, duplicates, and formatting errors.

Data migration is the process of transferring data from one system or database to another. Together, these processes ensure that your data is accurate, complete, and accessible, so you can get the most value out of it.

There are a few common scenarios when a business may need data cleansing and migration:

  • When you’re transitioning to a new system or database
  • When you’re merging with another company or organisation
  • When you’re consolidating multiple databases or systems
  • When you’re dealing with large amounts of outdated or incorrect data

The data cleansing and migration process

There are a few common myths about data cleansing and migration that we’d like to dispel:

Myth 1: It’s a one-time process

Data cleansing and migration are ongoing processes that need to be regularly monitored and maintained to ensure your data is accurate and up-to-date.

Myth 2: It’s easy

Data cleansing and migration require specialised skills and tools to ensure that the process is thorough and accurate.

Myth 3: It’s not important

Data is the lifeblood of your business, and accurate, accessible data is essential for making informed decisions and driving growth.

So, what does a typical data cleansing and migration process look like? Here’s an overview:

  1. Assess your data needs: We start by understanding your business, goals, and data requirements.
  2. Identify and correct errors: We use specialised tools and techniques to identify and correct errors and inconsistencies in your data.
  3. Migrate your data: We transfer your data to the new system or database, ensuring that all data is properly formatted and accurate.
  4. Test and validate: We thoroughly test and validate the migrated data to ensure that everything is working as it should.
  5. Ongoing maintenance: We provide ongoing support and maintenance to ensure that your data stays accurate and up-to-date.

Our expertise.

Our team of data experts has the skills and experience to help you with all your data cleansing and migration needs.

Whether you’re transitioning to a new system, consolidating multiple databases, or dealing with large amounts of outdated data, we can help you get the most value out of your data.

Ready to unlock the full potential of your data?

We’ll get you from point A to point B in good shape.

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Data collection

Capturing your first-party data in all the right places, and finding third-party sources that will work for you while respecting your customer’s right to privacy.

What is data collection?

Data collection is the process of gathering information from various sources to be used for analysis and decision-making.

There are two primary types of data: first-party data, which is data that you collect directly from your own customers or systems, and third-party data, which is data that you purchase or license from external sources.

You’ll also sometimes hear the distinction made between 0th and 1st-party data. 0th-party data is data your customer have directly opted to give to you (customer surveys), while 1st-party data is data you collect on your customers – think website traffic. In most practical scenarios, 0th and 1st party data are very similar.

There are also 2nd-party data and 3rd-party data distinctions. 2nd-party data is data exchanged between two organisations with a trusted relationship, while 3rd-party data is data simply exchanged and sold on a marketplace or via a transactional relationship.

Data collection process.

Gathering data can be a complex and time-consuming process, but we have the tools and expertise to make it easy. Here’s a look at our typical data collection process:

  1. Identify your data needs: We start by understanding your business, goals, and data requirements.
  2. Collect first-party data: We help you capture your first-party data in all the right places, using tools like web analytics, customer surveys, and CRM systems.
  3. Find third-party data sources: We help you identify and evaluate third-party data sources that align with your business goals and data needs.
  4. Integrate and organise your data: We help you integrate and organise your data, ensuring that it is accurate and accessible.
  5. Ongoing support and maintenance: We provide ongoing support and maintenance to ensure that your data stays up-to-date and accurate, and aligned with your organisational data strategy.

Practical examples.

A retail company might use web analytics tools to collect data on customer behaviour on their websites, such as the pages customers visit, the products they view, and the actions they take. This data can help the company understand customer preferences and identify areas for improvement on its website.

A healthcare organisation might use patient portals and electronic medical records to collect data on patient visits, diagnoses, and treatment plans. This data can help the organisation track patient outcomes, identify trends, and optimise resource utilisation.

A non-profit organisation might use surveys and focus groups to collect data on the effectiveness of its programs, donor attitudes, and volunteer engagement. This data can help the organisation measure the impact of its programs, identify areas for improvement, and engage donors and volunteers more effectively.

A manufacturer might use sensors and IoT devices to collect data on production efficiency, equipment performance, and resource utilization. This data can help the company identify bottlenecks in their production process, optimise its operations, and reduce costs.

The data collection process involves gathering information from various sources, and the specific sources and methods used will depend on the needs and goals of the organisation.

Collect it before you need it. Don’t drop your data on the ground.

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Data warehousing

Consolidating multiple sources of data into one consistent, accurate, and accessible location.

Your business generates vast amounts of data every day, but if you’re not storing and organising it properly, you’re not getting the full value out of it. That’s where data warehousing and data lakes come in.

What are data warehouses, data lakes, and data lakehouses?

A data warehouse is a centralised repository of structured data that is used for reporting and data analysis. It allows organisations to store large amounts of data in a single location, and to access and analyse that data quickly and easily.

A data lake is a centralised repository of structured and unstructured data that is used for big data analytics. It allows organisations to store and process large amounts of data in a single location, and to access and analyse that data using a variety of tools and techniques.

A data lakehouse is a hybrid approach that combines the features of a data warehouse and a data lake. It allows organisations to store and process both structured and unstructured data in a single location, and to access and analyse that data using a variety of tools and techniques.

For most small-to-medium organisations, a data warehouse is the right fit. Data lakes and data lakehouses tend to fit better for large enterprises, generating volumes of data that would overwhelm most data warehousing solutions.

When data warehouses make sense for you.

There are a few common scenarios when it might be the right time to invest in a data warehouse or data lake:

  1. When you have large amounts of data: If you’re dealing with large volumes of data that are difficult to manage and analyse, a data warehouse or data lake can help you store and organise your data in a centralised repository, making it easier to access and analyse.
  2. When you need to integrate data from multiple sources: If you’re working with data from multiple systems or databases, a data warehouse or data lake can help you integrate and organise your data in a single location, making it easier to analyse and get insights.
  3. When you need to support advanced analytics: If you’re looking to use advanced analytics tools and techniques, such as machine learning or big data analytics, a data warehouse or data lake can provide the storage and processing power you need.
  4. When you need to improve decision-making: If you’re looking to improve your decision-making process by getting better insights from your data, a data warehouse or data lake can help you analyse and visualise your data in a way that makes it easier to understand and act on.

Ultimately, the decision to invest in a data warehouse or data lake will depend on your specific business needs and goals. We’ll carefully evaluate your data requirements and determine if a data warehouse or data lake is the right solution for you.

Practical examples.

Consider our friend ACME Manufacturing Inc. who, like many manufacturers, runs multiple operating systems to manage their workload.

  1. The manufacturer is dealing with large volumes of data from multiple systems, such as production data, financial data, and customer data. This data is difficult to manage and analyse, and it is hindering the company’s ability to make informed decisions.
  2. The manufacturer decides to invest in a data warehouse to store and organise its data in a centralised repository. The data warehouse integrates data from multiple systems and allows the company to access and analyse its data more easily.
  3. The manufacturer uses the data warehouse to identify bottlenecks in its production process and optimise its operations. By analysing data on production efficiency, cost trends, and supplier performance, the company is able to reduce costs and improve efficiency.
  4. The manufacturer also uses the data warehouse to identify new product opportunities and target marketing efforts. By analysing data on customer demographics, sales trends, and product performance, the company is able to identify new products and markets to pursue.
  5. The manufacturer sees a significant ROI from its data warehouse investment. The company’s improved decision-making and optimisation efforts lead to increased sales, reduced costs, and improved customer satisfaction.

This is just one example, but the possibilities for using a data warehouse to drive ROI are endless. By storing and organising its data in a centralised repository, almost any type of business can gain insights and make informed decisions that drive growth and improve efficiency.

So why only see part of your picture?

We’ll help you bring together your data sources, and help you soar to new heights.

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Analytics platforms

What are analytics platforms and business intelligence platforms?

Analytics platforms and business intelligence tools are software applications that help businesses collect, store, and analyse data to make informed decisions. These tools can be used to visualise data, build reports and dashboards, and perform advanced analytics, such as predictive modelling and machine learning.

A customer data platform (CDP) is a specialised type of analytics platform that is designed to help businesses manage and analyse customer data. CDPs allow businesses to collect and integrate data from multiple sources, such as web analytics, CRM systems, and social media, and use that data to create a single, comprehensive view of their customers.

Setting up an analytics platform or business intelligence tool.

There are a few steps involved in setting up an analytics platform or business intelligence tool:

  1. Identify your data needs: The first step is to understand your business goals and data requirements. This will help you determine which type of tool is right for you and which features you need.
  2. Choose a tool: There are many different analytics platforms and business intelligence tools available, so it’s important to research and compare different options to find the one that best meets your needs.
  3. Integrate your data: Once you have chosen a tool, you’ll need to integrate your data into the platform. This typically involves extracting data from multiple systems and databases, cleaning and transforming the data, and loading it into the platform.
  4. Set up reports and dashboards: With your data integrated into the platform, you can start building reports and dashboards to visualise and analyse your data.
  5. Use the more advanced features: With many analytics platforms, advanced features like machine learning models and customer segmentation are built right in. Using these features will mean you’re well on your way to being a data-driven, modern organisation.
  6. Ongoing maintenance: Analytics platforms and business intelligence tools require ongoing maintenance to ensure that your data is accurate and up-to-date. This may involve regular data updates, data cleansing, and monitoring and troubleshooting.

Industry-leading platforms.

There are many analytics platforms that are well-suited for B2B and B2C organisations – whether you play in the high or low-volume game. Here are a few of the market-leading business intelligence tools:

  1. Tableau: Tableau is a powerful analytics platform that allows users to visualise and analyse data in a variety of formats, including charts, maps, and dashboards. It is particularly well-suited for B2B and B2C organisations, as it offers advanced features for data blending and collaboration.
  2. Power BI: Power BI is a hybrid cloud and desktop-based analytics platform sitting within the Microsoft service offering. It offers data pipelines and reports development, enabling end-users to build reports and dashboards using pre-qualified and endorsed data sets. Given it is a Microsoft product, this BI tool is well-suited to organisations already using Microsoft products for other parts of the business.
  3. Looker: Looker is a data platform that siting within the Google service offering. It is cloud-native, allowing allows users to build custom dashboards and reports using data from a variety of sources in the cloud. Alongside Google’s data warehousing capabilities in the Google Cloud Platform, Looker is a formidable option for organisations, particularly those using Google products for their day-to-day operations.

Each of these business intelligence tools is a great option, and often the selection is made primarily on the basis of existing software in use at the organisation – usually Microsoft or Google.

For organisations collecting behavioural data, a behavioural analytics platform like Amplitude or Mixpanel may make more sense than – or complement – the above business intelligence tools. In contrast with business intelligence tools which work best with structured rigid datasets, behavioural analytics platforms are designed to help businesses track and analyse user behaviour and engagement. They are particularly well-suited for companies that want to understand how users interact with their products or services in detail. They specialise in exploratory analysis and root-cause analysis-driven analysis, uncovering insights from the largest datasets imaginable.

Both Amplitude and Mixpanel offer direct integration with many leading marketing automation platforms, closing the feedback loop between analytics and marketing implementation.

Practical example.

Let’s talk about ACME Widgets, a B2C organisation that sells propane and propane accessories. ACME’s lowest-hanging fruit is using an analytics platform to improve its sales and marketing efforts. By integrating data from their CRM system, web analytics, and social media, the organisation can create a single, comprehensive view of their customers and their needs.

Using the analytics platform, the organisation can build reports and dashboards to visualise and analyse data on customer demographics, sales trends, and product performance. This can help them identify new product opportunities, target marketing efforts, and optimise their sales process.

The organisation can also use the analytics platform to perform advanced analytics, such as predictive modelling and machine learning. For example, they could use predictive modelling to identify which customers are most likely to make a purchase, and then target marketing efforts towards those customers.

If ACME chooses the right platform, then the platform may even assist in the experimentation and optimisation of its app, website and marketing content – directly measuring and reporting on the results in a statistically significant manner.

By investing in an analytics platform, ACME can drive growth and improve efficiency, leading to a positive ROI for their investment.

So why settle for less?

We’ll help you choose the right analytics platform for your organisation.

Analytics isn’t reporting any more

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In the not-too-distant past, analytics used to be seen as after-the-fact reporting, dashboarding, charts — understanding what had already happened. Today, analytics is much more tightly coupled with business strategy and product development. It is used to inform and guide decision-making in real-time. 

This shift is due in part to the increasing availability of data. There are now more data sources than ever before, and they are producing more data than ever before. This means data is being generated at an ever-increasing pace, making it difficult for many businesses to keep up, let alone take full advantage of the possibilities that lie at their feet. 

It’s not just the volume of data that has changed, it’s also the quality. Data is now more accurate and timelier, thanks to advances in data collection and processing technologies. This has made it possible to use data to understand not just what has happened in the past, but also what is happening right now and what is expected to happen in the future. 

This shift has had — or, in too many cases, ought to have — a major impact on the way businesses operates. In the past, businesses would make decisions based on their own experience and intuition. Today, they should be increasingly incorporating data-driven insights to guide their decision-making. 

This change has been driven by the need to be more agile and responsive to the ever-changing and competitive market. In the past, businesses could afford to take their time to make decisions. Today, they need to be able to make decisions quickly, based on the latest data. 

This change has also been driven by demands to be more efficient and accurate in investments and use of resources. In the past, businesses would often make decisions based on hunches or guesswork, and there’s a certain “they did the best they could do” asterisk against such decisions that turned out poorly. 

Today, with the high-quality and real-time data available in every business sector globally, a growing expectation is placed on decisions being correct and backed by the data. 

Hopefully it’s becoming clear that organisations need to embrace the capabilities made available to them with modern analytics. However, modern analytics isn’t delivered by simply hiring an analyst; organisations need to consider how their analytics function interweaves with the business as a whole. 

In many cases, analytics can neatly sit alongside both the business strategy and operations teams, forming a vital link between these levels of an organisation. Other times, a business partner approach can work well. In any case, an organisation’s analytics function must be underpinned by investment in systems, processes, and most importantly of all, the business culture. 

Business culture means promoting a data-driven mindset throughout the organisation, from the boardroom to the front line. It means everyone in the organisation understands that data should be used to inform their own decision-making. 

It also means creating an environment where it’s safe to experiment, where failure is seen as a learning opportunity rather than a cause for punishment. And it means having the right people with right skills in place. Communication skills and data literacy are as equally important as technical prowess. 

Consider if an internal analytics function is right for the scale of your organisation. In many cases, analytics isn’t a core business skill, and the investment required to manage this in-house isn’t worth it; good analysts and systems don’t come cheap. Quality analytics-as-a-service offerings can deliver great outcomes while remaining cost-effective. 

Ultimately, there is no one-size-fits-all solution when it comes to utilising analytics into an organisation, but there are some key principles that all organisations should bear in mind: 

  1. Modern analytics deserves to be tightly-coupled with business strategy 
  2. Data can and should be used to inform and guide decision-making in real-time 
  3. Organisations should take advantage of the increasing availability and quality of data 
  4. Move beyond just analysing what’s happened in the past. Instead, look towards the future. 

Consolidating datasets to stem the app addiction

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It’s fair to say that most businesses are onboarding more software and apps than they’re offloading. Okta’s 2021 Business at Work report clocks the average business as using 88 apps! That’s a lot of apps – and a lot of data sources. 

In this blog, we’re going to take a look at the obvious and not-so-obvious benefits of consolidating datasets from your business apps. We’ll cover how it makes information more visible for your decision making, how it brings consistency and control to your processes, how it improves your compliance and security, and (usually the first question asked) how it’ll provide a return on investment for your business. 

Information visibility 

We won’t spend too much time discussing the benefit of information visibility, because it’s probably the most obvious and certainly the most spruiked benefit of consolidating your dataset. We’d like to spend more of your time discussing the other benefits that don’t get as much of the limelight. 

But nonetheless, it is a big benefit, because better decisions are always made when more data is available. Would you have eaten at that dodgy restaurant if you had access to the health inspectors’ data? Probably not! 

The same can be said for your business; if you’re only looking at one system, you’re not getting the full picture. And remember the saying: “the whole is greater than the sum of its parts”. 

Consistency and control 

The best standard operating procedures in the world still can’t prevent the human error that arises when the procedure is repeated regularly. It’s why your insurance company passed you over to the wrong department (and set you back in the queue!), or you realise you forgot to put milk in your coffee. 

If a human oversees manually collating a dataset each week for your weekly reports, it’s almost guaranteed that an error will work its way in eventually. Humans are fallible. 

Computers, on the other hand, are surgical. They’ll do the same thing you ask them to do, day in, day out, and exactly the same way every single time. That’s why an upfront investment in automating your data consolidation is so worthwhile – do it right once, and it’s done forever (maintenance notwithstanding, of course). 

In practical terms, this means you can be confident in the metrics you’re reporting to the board because you know the dataset was assembled correctly. On a broader scale, it also means everyone in your organisation is reporting off the same dataset, so you avoid the confusing scenario where your metrics conflict with another analysis because each had slightly different methodologies behind them. 

Productivity & return on investment 

It’s all too easy to fall into the trap of your weekly reporting taking up far too many working hours – Excel spreadsheets, .csv files, weird custom data formats (and hopefully not paper forms!) all need to be collated, combined, and analysed manually each time you need an answer. 

If that sounds like your situation, have you taken the time to consider the true cost to your business? For the sake of argument, let’s say the person preparing the pack each week is on $100k and is spending about a day collating the pack – each report pack costs almost $500! 

Now, sure, the report is usually important enough that they can’t be cut. But because these packs are usually pulling the same data sets together each time, they’re prime candidates for automation. Let’s let the computers do what they’re good at: repetitive tasks with surgical precision. 

The beauty of such an automated process is that you release a full day of resources to work on something much more valuable – be it more in-depth analysis on specific business opportunities, focusing on new views of data for better insight, or in the case of many smaller business, just doing their core job (which probably has better ROI than collating .csvs and Excel spreadsheets!) 

Compliance and security 

With ever-growing public awareness of privacy and associated data security, more and more people exercise their “right to be forgotten“. While not a legislative requirement everywhere yet, recognising and acting on these requests should be seen as an important aspect of being a good community citizen. But do you know every location where your customer’s data is stored? And would you be confident in asserting that you have expunged all this information? Even worse – how many uncontrolled datasets are floating around in company laptops and network drives because they were used as part of an analysis pack? What are you doing to protect your customer’s data (and your business interests) from such a serious data breach risk? 

Consolidation of your datasets into a single location vastly simplifies all these problems. You’ll no longer need to provide people access to each system’s backend individually, nor will they need to export the data. Instead, you’ll have a unified, consistent dataset sitting centrally (usually on the cloud) where you can hook up your business intelligence tools directly. 

This also simplifies your approach to security – does your analyst actually need to know your customer’s street address, or will just seeing postcode suffice? You can create custom column and row slices of this centralised dataset and provide only the relevant slice to the relevant stakeholder in your business. No more “all or nothing” here! 

Hopefully we’ve been able to open your eyes up to some of the finer details around the benefits of consolidating your datasets. While seeing more of the big picture is certainly a massive benefit, the others in compliance, security, and productivity should each be a consideration in your investment. 

If this sounds like an initiative you’d like to undertake, we’d be happy to design your data strategy, guide you through the execution, and help you realise all of these benefits and more. 

Contact us here.

Why “I don’t have any data” is a cop out

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An all too common phrase heard across almost every business and industry is “we don’t have any data to analyse”. This couldn’t be further from the truth; I can assure you that almost every business does have data to analyse. You don’t need to be a Google-scale operation – you just need to broaden your horizons on what useful data is. Once you’re done reading through this post, you might find it hard to stop thinking about all the various places you’re generating and collecting useful data already! 

Let’s take a quick step back and lay the foundations first. We want to use analytics to give us actionable insights: “If your goal is X, you should do Y to achieve it”. To get to this (incredibly powerful) state, you’ll first need to have three key pillars firmly in place: 

  • Data strategy: Why are you doing this initiative? How can you use data and its insights to inform, support and execute your business strategy? 
  • Data management: Where and how are you storing the data, validating it, securing it, and making it easily available for quick and useful analysis? 
  • Data analysis: Given the right data in the right format, what actionable insights can be extracted? 

So, when a business says, “we don’t have any data to analyse”, it’s actually often attributable to focusing too much on the analysis instead of the strategy and management. One of the key steps in developing the latter two is to broaden your horizons and truly understand the breadth and depth of data you’re already collecting (alongside future data collection initiatives, when they align with your business strategy). 

Now with those foundations laid, let’s run through some useful datasets that almost every business will be generating and what you can learn from them. 

Financial and customer data 

For most businesses, this is probably going to be the most reliably collected data available. After all, it’s mission-critical; without the data on who owes you money, your business is in a lot of trouble! 

The data you generate managing your accounting and commercials can help form actionable insights like: 

  • Data on who are you spending the most money with → Use this to determine where cost optimisation efforts could be placed 
  • Data on who is spending the most money with you → Use this to determine where account management efforts could be placed 
  • Data on how often and how much your customers are spending → Use this to determine if your clientele is aligned with your strategic goals, or if you need to source a new client base 
  • Data on how customer spend behaviour has changed over time → Use this to see if newer customers are more or less profitable than older ones (and determine if you’re acquiring customers from the right source) 
  • Data on how often a specific customer is usually buying from you → If they’ve missed their timeframe, you could use this opportunity to re-engage with them to win them back (bonus points if you can set up marketing automation to complement this at scale) 
  • Data on what products and services are usually bought together → Use this to understand what bundling options to approach the market with, or perhaps gauge untapped bundles that you should be upselling 

Business sentiment 

Once you get a few customers through the door, it’s likely your customers will start talking about you either directly via feedback, or indirectly via reviews or general online discussions. 

Some actionable insights that can be extracted from this data include: 

  • Data on the sentiment of the feedback / online discussions → Use this to understand if your customer engagement and experience is actually in line with your strategic goals 
  • Data on which parts of your business are associated with positive/negative feedback → Use this to find any focus areas where you need to improve your customer engagement 
  • Data on “black spots” where customers using a specific part of your business aren’t engaging → Use this to analyse and better service customers using this product 

Marketing data 

If you’ve taken the leap and started actively marketing your business (if not, we’d be happy to chat), then you’re certainly generating valuable data that can steer your marketing efforts towards long-term success. 

Most digital marketing campaigns will collect: 

  • Data on how often your customers are being contacted (i.e. your marketing schedule) → Use this to understand the optimal frequency to contact customers (once or twice per week, or once a month) 
  • Data on when customers engage with your content → If you’re a B2B business then you may be better off sending your marketing emails during the working day, while B2C may suit after-hours emails – but this isn’t a hard and fast rule! Your engagement data – like open and click-through rates – can tell you what’s working for your specific customer base already 
  • Data on the content that resonates with each segment (a good base-level approach) and each person (a more advanced analytical approach) → Use this to send the relevant types of content to each segment/person to maximise their conversion to your product and overarching marketing goals 

Recap

Hopefully, I’ve been able to demonstrate that the saying “we don’t have any data to analyse” usually couldn’t be further from the truth. Once you broaden your horizons on what useful data can be, you’ll find most businesses sit on a wealth of data. It often just needs a little attention – via the development of a sound data strategy and data management practices – before powerful actionable insights can be extracted. 

I’d encourage setting aside some time to reflect on the above and, equally, contemplate what data your business generates by virtue of being the unique business that it is – it’s a competitive necessity in an increasingly data-driven world!

Contact us today for more information on how we can work together to create data-driven, actionable insights to help your business succeed.

Freighter airplane being loaded with cargo.

Securing and managing Australia’s PPE supply chain

In early March 2020, Vaxa Analytics and Vaxa Bureau were contracted by Aspen Medical to provide procurement and logistics advisory support to fulfil their obligations in restocking the National Medical Stockpile with vital Medical PPE.

The mission was clear – to secure supply chains and facilitate the procurement and safe passage of vital medical PPE offshore to Australia in a very short timeframe.

Upon receiving an initial briefing, including an overview of the extent of existing systems, tools and processes, the task and context of the requirement changed rapidly, requiring additional skill sets and personnel, namely data analysis and software design to support the effort.

Our compact, nimble team of data analysts, software developers, procurement, supply chain and logistics personnel worked closely with the Aspen team to develop a robust plan which included the need to build bespoke software to manage the entire process, ensuring goods were Delivered In Full and On Time.

We undertook tasks including modelling, logistics management, contract negotiations, data analysis and management reporting, supplier vetting, supplier management, facilitation of quality control mechanisms, communications to the Commonwealth and more.

Underpinning the overall success of the program were Vaxa Analytics’s data and software capabilities. Our team of specialists evaluated and strategised the software and data required to support project outcomes.

We built a bespoke ERP system to manage and report on logistics and business intelligence to enhance data capture capability which enabled the executive team to make accurate and informed decisions. The system need was analysed, developed, tested and deployed in less than two days to meet ambitious targets set by the Government, and was a vital component in managing the successful program.

The system managed the collection and stewardship of data and, via integration, fed live reporting through to the preferred reporting suite.

In doing so, the program enabled the management of critical components including manufacturing timelines, essential compliance certifications and quality programs, and freight management components to allow for efficient load mastering, air charters, contract management, and delivery of goods into the NMSP across hundreds of stock movements by ground, sea and air from dozens of suppliers. It also served a historical record, demonstrating value-for-money delivered for the National Medical Stockpile.

The outcome

Whilst other governments and commercial organisations were facing challenges to analyse and deliver their critical goods, working in conjunction with the various actors in the program, we delivered all items in full and on time, providing the Commonwealth with confidence in the systems to procure further items through this supply chain.