Analytics teams support value creation in growth-stage portfolio companies by improving revenue quality, margins, cash flow, and decision discipline. For private equity and venture capital firms operating in a higher-interest, lower-multiple environment, analytics is now a core input into value creation planning rather than a supporting capability.

This article explains where analytics contributes most directly to commercial outcomes, why progress often stalls, and how operating models and talent choices influence results.

On this page:

• • Why analytics matters now for private equity portfolios
  • What value creation through analytics looks like in practice
  • How AI and analytics operating models affect portfolio-level value
  • Why analytics initiatives stall in portfolio companies
  • How stronger analytics teams overcome these issues
  • How to structure analytics teams for value creation
  • What this means for operating partners and investors

Why analytics matters now for private equity portfolios

Value creation has shifted inward. Longer hold periods, higher financing costs, and closer scrutiny of forecasts mean performance must be supported by stronger internal controls and clearer visibility into how the business operates.

Recent survey data underscores the pressure. In the North America Value Creation in Private Equity Report 2025 from Alvarez & Marsal, only 31% of respondents reported a positive outlook on deal activity over the next 12 months. 72% realized less than 75% of planned value, and 55% are now investing in value creation initiatives more than one year into the hold cycle.

In this environment, analytics is increasingly used to:

• Improve confidence in revenue and margin forecasts
• Identify operational inefficiencies earlier
• Support pricing, cost, and working capital decisions
• Strengthen exit narratives with evidence rather than assumption

Across private equity research, a consistent pattern emerges: analytics contributes most when ownership is clear, priorities are commercially defined, and teams are positioned close to the decisions that affect revenue, cost, and cash flow. 

What value creation through analytics looks like in practice

Revenue quality and pricing discipline

Analytics supports revenue performance by improving the quality and consistency of commercial decisions, rather than driving volume alone. In PE-backed businesses, this most often shows up in areas such as:

• Customer and product segmentation
• Pricing visibility and discount governance
• Regional or channel-level sales performance analysis

Where analytics capability is positioned close to commercial leadership, these approaches help reduce decision variability and support more disciplined margin management over the hold period.

Cost and margin control

Operational analytics often contributes early to margin improvement because it focuses on reducing variability rather than changing behavior at scale. Typical use cases include:

• Predictive maintenance in asset-heavy environments
• Demand and capacity forecasting
• Automation of repeatable finance and operational processes

These initiatives tend to be tied to clearly defined cost drivers, which makes outcomes easier to track and manage.

Working capital efficiency

For capital-intensive portfolio companies, analytics frequently delivers value through improved cash management. Common use cases include:

• Inventory optimization
• Forecasting accuracy improvements
• Reductions in excess stock or expedited procurement

These initiatives tend to be easier to govern and measure than broader transformation programs because they are directly linked to cash flow and operational efficiency.

Data monetization, where appropriate

Data monetization is not relevant to every portfolio company. Where it does apply, it typically follows earlier investment in data quality and operational analytics. Examples include:

• Benchmarking products
• Embedded customer insight services
• Data-led product extensions

This type of value creation tends to emerge later, once core reporting and decision support are stable.

How AI and analytics operating models affect portfolio-level value

For operating partners and private equity leadership, one of the most consequential analytics decisions is not technical but structural: who owns analytics, and at what level.

Research from FTI Consulting identifies four common operating models, defined by the degree of centralization across the portfolio:

• Decentralized: each portfolio company owns analytics independently
• Center of Excellence (CoE): one or more portfolio companies act as capability hubs
• Fund-specific: shared analytics capability across a subset of assets
• Centralized: firm-level ownership of policy, priorities, and platforms

Portfolio-level value creation depends on how effectively knowledge, talent, and repeatable use cases can be shared across assets. Firms that move incrementally toward greater centralization, particularly around policy, prioritization, and architecture, are better positioned to reuse what works, rather than rebuilding analytics capability asset by asset.

Why analytics initiatives stall in portfolio companies

Many PE firms are asking, “What’s the right way to use AI in value creation?”

It’s one of the most controversial questions in private equity. Not because AI lacks potential, but because too many initiatives start with use cases rather than readiness. More often, it is timing, leadership, and alignment with the value creation plan.

Common constraints include:

• Fragmented systems and inconsistent data definitions
• Legacy infrastructure that limits integration
• Teams positioned too far from commercial decision-makers
• Lack of senior ownership for outcomes

As Gavin Geminder, Global Head of Private Equity at KPMG, notes:

“Having clear, ethical AI guidelines in place is going to build employee trust and customer satisfaction, while also enhancing GPs’ brands.”

In FTI Consulting’s AI Radar for Private Equity 2025, 36% of PE firms with an AI strategy reported having no specific milestones or KPIs to measure impact on value creation. Without clear ownership, success measures, or prioritization discipline, initiatives tend to accumulate as pilots rather than translate into sustained operational change.

How stronger analytics teams overcome these issues

High-performing portfolio companies take a deliberate, value-led approach.

Focus on defined, near-term use cases

Initiatives are selected based on expected commercial impact within the first 6–12 months, aligned to the investment thesis.

Embed analytics into commercial and operational teams

Analytics works alongside sales, operations, and finance, with shared accountability for outcomes rather than downstream reporting.

Align management, operating partners, and investors

Priorities are reviewed regularly to ensure analytics remain tied to the value creation plan as the business scales or changes direction.

How to structure analytics teams for value creation

Team structure and leadership choices play a significant role in whether analytics contributes to value creation.

In growth-stage portfolio companies:

• Analytics leadership often reports into the CFO or COO initially
• As scope increases, responsibility may move to a dedicated Head of Analytics or Chief Data Officer with board-level exposure

Sequencing matters more than team size. In many cases:

1. A commercially credible analytics lead is hired first
2. Data engineering capability is added to improve reliability and scale
3. Applied data science is introduced where specific use cases justify it

Hiring technical depth without sufficient commercial context is a common cause of slow progress. Many PE-backed businesses use market benchmarks, such as Harnham’s Data & AI Hiring Guide, to sense-check seniority, expectations, and retention risk through the hold period.

Source: Harnham’s How to Hire in AI

What this means for operating partners and investors

Analytics should be treated as part of the value creation plan, instead of a standalone capability.

Useful questions to ask portfolio leadership teams include:

• Which commercial decisions does analytics directly support?
• Who is accountable for outcomes, not just reporting?
• How does analytics align with the investment thesis and exit plan?

When these questions are addressed early, analytics is more likely to support sustained performance improvement.

Analytics value creation quick reference

How Harnham supports analytics-driven value creation

Across private equity portfolios, the real challenge is how teams are structured, led, and scaled in line with the value creation plan.

Harnham supports private equity and venture capital firms by helping define analytics leadership requirements, assess team structure, and benchmark roles as portfolio needs evolve. Our work focuses on hiring decisions that support commercial priorities, operating discipline, and long-term exit readiness.

For firms reviewing analytics leadership or team structure across portfolios, you can explore Harnham’s analytics hiring capabilities here or get in touch for a market-led discussion.

The post How Analytics Teams Drive Value Creation in Growth-Stage Portfolio Companies appeared first on Harnham.

One of our consultants, Lottie Musgrove, sat down with Chris Hopkinson to learn about his journey into data engineering, and how he managed to break into this extremely competitive field.

Hopkinson has been working in data and analytics for nearly a decade. Currently working as a Lead Data Architect/Consultant, Hopkinson is starting a new role as Head of Data Engineering and BI at Evri soon. Here’s a summary of Musgrove and Hopkinson’s discussion.

Question #1: What motivated you to pursue a career in data engineering?

I used to be an accountant, but I didn’t really enjoy the role. So, I started thinking about what I actually enjoyed, and what I wanted to get out of work. And I realised that I was really interested in how we reported information as accountants, how that information flowed around the businesses, and then how we could improve those information systems.

I’d already started to teach myself SQL and was already starting to automate things in my accounting role to help optimise our processes. So, I realised that there was something there, that this was an area that I was really interested in, and also something that could have a real
business impact.

So, I went ahead and did a master’s in what was then called Business Intelligence and Data Mining, and that’s really what brought me into the world of data.

Question #2: What were some of the challenges that came up for you during your transition from accounting to data?

For me, it was challenging to get the right type of experience in the early days of my data career, and to find a company that was willing to take a chance on me, to allow me to get the hands-on experience I needed to “break in”. Eventually, I was fortunate enough to have someone take me under their wing, and allow me to join their boutique consultancy firm, which gave me an opportunity to work on data projects
with big companies and ultimately get my foot in the door.

Question #3: What have been some of the major milestones or turning points in your career?

My first proper role in analytics was a big deal, but I think the advent of massively online courses really opened the flood gates in terms of learning. I remember going online to learn about R, machine learning, and just finding the whole world of online resources and courses quite fascinating. The online resources I found gave me the opportunity to learn about things that I would have absolutely no chance of learning otherwise. Obviously now, there’s a plethora of material available online, but I feel fortunate that I found those online resources when I did, and that I was able to build my knowledge base around that.

Another real milestone for was getting access to tools like Hadoop and the world of big data. Before then, everything was either in a server or a database, whereas Hadoop and big data created an opportunity to start to do things at a much larger scale. This allowed us to look at and analyse things like network data. It was a learning curve but it really enlightening, it really opened up a world of possibilities. And finally, AWS and the cloud was a big milestone that stood out to me.

The cloud allowed me to start thinking in more of a system/architecture basis, which really changed my idea of how we can build systems, and what we can do to give our organisation the best chance of garnering valuable insights from its data.

Question #4: What do you do to keep continuing your own development?

I think the challenge now is finding the signal from the noise. There are so many people talking about data, writing blogs, and so much hype around AI, that finding the actual useful information can be difficult.
Because anybody can use a generative model to summarise something that someone else has written, I think it’s important to look at the credentials of the people that you’re following and make sure they’ve got the kind of experience that means they can talk into the area of experience that they profess to have.

I look for people that are working for companies that are building data products, or people that are leading AI consultancies – people that can share ideas and trends they’re sending first-hand as industry experts.

Question #5: Since you started your career, what have been some of the biggest changes in the industry that you’ve noticed?

It’s moving away from a small and narrow stack that most companies use, to a plethora of different tools that are available through the cloud.
Before, you could learn a tool like the SQL server stack, and pretty much be set for life. But now, as more companies adopt the cloud, the number of different services available has skyrocketed, which has changed what you have to do (and what you have to know) as a data
engineer.

Now, you need to start thinking in terms of systems more and more. There are also the changes that have come about through the adoption of software engineering principles, and the creation of DevOps and CI/CD. I think that data engineering as a field is still
on a path of discovery. We’re still trying to figure out how all these different specialisms
intersect and work together.

Question #6: How do you stay passionate about a field that’s constantly changing?

I really enjoy learning new things, so learning a new programming language is fascinating to me. What I see is that people who are willing to invest in continual learning really stand out in what’s becoming a really complicated landscape. For me, I really need to see my work have an impact on the business. And one of the nice things about engineering is that it’s a practical application of technical knowledge to business
problems.

So, making sure my work has some impact on the business keeps me focused and grounded on the things that matter. Working with other people that are passionate really helps too. You can get inspired by people that are excited about something, and by people who are good at what they do, and you can play off each other.

Question #7: How do you see the role of data engineer evolving?

There could be more separation into specialisms because there comes a point where there’s too much knowledge to expect from a single role.
We’re already seeing this already with the separation of analytics engineering and data engineering, so we may see more things that emerge like that.

Question #8: Are there any trends that you think data engineers should be focusing on right now?

Different paradigms of storage (S3 vs. database vs. document store) and processing (via Spark, a single machine, or a streaming platform). Getting a good grounding of all those things will help you make good decisions on what you should be using for a particular business problem that you’re trying to solve.

Also, while you don’t need to become a data scientist, gaining an understanding the needs of that domain will help you understand what you need to know about in order to add value to these different things that are happening in the business.

Question #9: What kind of advice do you have for somebody trying to break into the field?

Firstly – go for it. Secondly, get your hands on something. If you’re struggling to get access to a data engineering role specifically, maybe look at the adjacent roles like analyst roles. Anything that involves writing SQL, touching data – whether that’s building reports or spreadsheets – just
something that gets you in that space.

If your role doesn’t have it already try to bake it into your role and show your manager what you’re doing. That might not work for everyone but that’s certainly the path I took. But definitely, get hands-on experience and start to think about how you can do this stuff in your current role, which will help in the future when you start to apply for new roles.

Are you a data professional that’s looking to make a career change, or break into the industry? Get in touch today.

The post Interview With Chris Hopkinson: A Deep Dive into Data Engineering appeared first on Harnham.

Each match is, of course, followed – and preceded and interrupted by – an analysis by experts of the game at hand, what is going right and wrong and speculation on what is to come. For this, our pundits are relying heavily on the plethora of data that is captured during every second of every game, as well as data on past performances, previous meetings of the teams on the pitch, player fitness, past World Cup statistics, the list goes on.

And this is just the broadcasters. The coaching teams are constantly collating and analysing data on which to base their training, game plans and tournament strategies.

Data analysis is sharpening every major sport in an evolving number of ways. Sport is big business, and, like any business, those at the helm rely on data to make sound, evidence-based decisions. Rugby is no exception.

Who are the analysts?

Since becoming a professional sport in 1995, rugby has evolved into the highly competitive, popular and lucrative sport that it is today. With this monetising of the game naturally comes the drive for a competitive edge for clubs to win those tournaments and sponsorship deals, and this is where data comes in.

All national teams playing at the 2023 Rugby World Cup will have their own data and analytics experts working round the clock to underpin their campaign. Any time the camera pans up to a coach during a match, they will always be flanked by multiple experts on laptops carrying out live match analysis to inform key decisions during play. And that is just on match day in host nation France. Thereafter, data will be shared with wider analytics experts, including ‘back home’, to continue analysing team and individual player performances to help coaching teams assess and plan ongoing training and how to best prepare for forthcoming matches.

Examples of the kind of data being captured will include speed of ball, how far ball carriers are gaining past the line and various other metrics that help to demonstrate how effective a team’s attacking or defensive game is.

As well as gathering data via inhouse analytics teams, third party specialist rugby data and analytics providers capture a staggering level of performance data. This can be used to train Machine Learning (ML) models to identify patterns and generate predictive insights of players’ performance and even expected scores. As well as supporting coaches, this helps broadcasters tell the story of the game for match day coverage and build up, and betting firms to set their odds.

With this technology revolution in rugby, the importance – and value – of data professionals is steadily increasing. Naturally, these individuals are likely to have an interest in the sport, however the emphasis here is on professionals with strong data and analytics skills who can translate what they find into useful information for coaching teams to use in their decisions. And they certainly have the ear of coaches. Their value is not just during team training and match day analysis and post-analysis, but also in assessing opposition team tactics and performance. Analysts can identify patterns in their upcoming opponents to better prepare for their strengths and capitalise on their weakness.

Recruitment

Naturally, a sizeable portion of a rugby team’s budget goes on the players, so choosing the players that will give them the best return on their investment is key. At a league level, the hope is always to snaffle that little known player who is on the cusp of greatness. During an international tournament, the head coach needs to know that the players in the squad have the right mix of skills that can be adapted and switched around to respond to evolving World Cup challenges, such as injuries or red cards and the conveyor belt of opposition teams that they may come up against.

Data and analytics play an integral role to player recruitment, because coaching teams cannot attend every league game to draw their own conclusions about players, or make important decisions based on sight alone. Data gathered about player performance on the pitch is crucial to making recruitment decisions.

Wearable technology

Rugby club coaching teams have embraced new technologies to improve their training programmes, including extensive use of sophisticated video analysis software, as well as GPS tracking devices, through which coaching teams can monitor the physical demands on a player during training to avoid injury or overexertion and ensure players are match-ready.

Phone apps are being used to track player wellbeing by recording, for example, their post-match recovery and other details such as mood and sleeping patterns. This allows coaches to gather important information to monitor the wellbeing of their players in a way that they would not have time to do on a one-to-one basis with a whole squad.

There have also been great advancements in player safety through technology. One example is impact sensors being built into players’ gum shields to help medics assess the likelihood of concussion following an impact to the head.

The future

The influx of wearable tech and hoards of data to analyse of course has its downsides. With such advancements, players can feel under constant pressure to achieve their personal performance indicators which are relentlessly tracked and shared, with an echo of Big Brother. These vast quantities of data on players’ personal performance also bring with them data security concerns and questions over ownership.

There are also arguments to suggest that an over-dependence on data removes the human element, leaving little room for the passion and intuitive magic of rugby legends of the past. But, as in all things, finding moderation and balance is the key and there is no doubt that technology and the use of data and analytics has pushed the game to a new level of competitiveness, as well as safety, something which is extremely important in contact sport at this level. Progress will not move backwards.

Each club and national team have their own relationship with data and how they use the myriad of different data sources. Continuously reviewing and defining this relationship will be key to managing the role and impact of data on players, clubs and future Rugby World Cup tournaments.

If you are interested in finding out more about what data analytics has to offer your organisation or would like to put your data analysis skills to work on the rugby pitch – get in touch with one of our consultants today.

The post Data Analysis in Rugby Union appeared first on Harnham.

However, it’s expected to grow even larger over the next decade. Alongside this growth, Harnham’s role in the Netherlands is also expanding—so much so, that we’re planning on opening a local office in the region in the coming months.

With this in mind, we thought we’d take a step back to reflect on how the region’s data recruitment market has evolved over the past year and touch on what’s expected for the rest of 2023.

Trend #1: An Emphasis on Cloud Tech Stacks

While there are plenty of job seekers in the current market, there are also lots of companies looking to make hires. Because of this, we are expecting to encounter a very competitive and fast-paced first quarter for 2023.

In the business intelligence market specifically, we’re seeing companies invest more time and resources into developing their cloud platform tech stacks. This trend is becoming increasingly apparent in both job postings and through our client interactions.

In our experience, bigger companies typically prefer to use a Microsoft heavy stack such as Azure, whereas the smaller scale companies and start-ups lean towards Google and Amazon platforms such as AWS or Google Cloud.

Trend #2: Increased Demand for Analytics Engineers

When it comes to available positions, we’re seeing an increasing demand for analytics engineers, a trend that has exploded in the UK over the last 12-18 months and is now being emulated in the Netherlands.

Essentially, companies are seeking someone with a blended skill set, who has the ability to build out and operate a platform, and then take that data and translate it in a way that makes sense to key stakeholders in the business.

The fusion of the different skill sets that are required for these roles can make them challenging to recruit for. Typically, candidates are more experienced in either engineering or data—not both.

Trend #3: More English-Speakers

Over the past year, we have seen many companies shift from seeking Dutch-speaking candidates to operating almost exclusively in English. Some companies have even translated their whole website into English.

However, this shift to English hasn’t necessarily been a choice. Many companies have made the switch to English simply because it’s been too difficult to find local talent. Many Dutch natives are seeking contract or freelance work over permanent roles because of their flexibility and higher pay.

While Dutch talent is growing scarce, English-speaking candidates are becoming easier to find, largely because of the tax advantage known as the 30% ruling that gives foreign workers a 30 per cent tax benefit for five years. This has increased the supply of international candidates across the Netherlands, which has further incentivised local professionals to look elsewhere.

Trend #4: Employees Want a Hybrid Model

The pandemic made working from home the norm, but more recently, candidates are finding themselves less receptive to remote working and instead craving in-office opportunities. This is because many people have experienced feelings of isolation and a lack of productivity while working fully remote.

However, some smaller organisations that we work with are still opted for an almost fully remote model, for various reasons such as cost savings, etc. And these companies that are mostly remote are finding it challenging to source talent.

What’s on the Horizon?

While no one has a crystal ball, economic experts anticipate that the Netherlands will experience a lighter recession that’s unlikely to hamper the data market’s growth. In fact, it is still forecast to expand.

The travel industry in particular is projected to do well in 2023. It’s experiencing a recovery post-COVID, so we could potentially see an influx in available data jobs with travel companies like hotel chains and online booking websites.

As mentioned, we are expecting to see many companies finally making steps into analytics. We even have clients that have been operating for over a hundred years and are now dipping their toes into the analytics space – an indication that the Dutch market is maturing when it comes to companies seeing the value of data.

Of course, these modernisations are often accompanied by skeptics. When companies have employees who have never made decisions based on data and are reluctant to do so, a consultative approach is required to encourage a mindset shift.

If you are interested in pursuing a data career in the Netherlands or are planning to expand your team, this is an ideal time to explore your options. Harnham is opening an office in Amsterdam and will be on the ground for face-to-face consultations. Get in touch with Ross Henderson to book a meeting.

The post The State of Data Recruitment in the Netherlands appeared first on Harnham.

Why You Might Need an Analytics Engineer

Data-driven businesses interested in building value for their customers often turn to a mix of Analytics and Data Modelling Engineer. The Data Engineer creates the infrastructure, platform development, and Data movement for the purpose of Machine Learning and Analytics downstream. Ultimately, the Analytics Engineer role is quite similar to the typical Data Engineer but differs in that it doesn’t involve platform development or infrastructure the same way.

Analytics Engineering is a relatively new term within the last five years and are coming into this field from a variety of backgrounds. But the most in-demand background moving into this role is Data Engineering. Why? For the most part, it’s those individuals who can not only script in Python but also do Python programming on the backend.

Key Aspects of this Role:

• Warehouse architecture (e.g., Snowflake, Redshift, BigQuery), and Data Modeling with a popular and relatively new tool dbt (originally Fishtown Analytics), for use by Analysts.
• ETL Development
• Data visualization
• Other tech such as Fivetran, Stitch, and Python

With SQL and Data modelling being the real meat and potatoes of the position, people often move into an Analytics Engineering position that requires little Python experience – however, the salary you can expect if Engineering or Data Science experience and proficiency in Python is substantially higher. It poses an interesting opportunity for Analysts, Data modellers, and Data visualization folks interested in learning a modern engineering stack to make a transition into a more technical, and higher-paying role.

Why You May Want to Consider an Analytics Engineering Role

People move into this role from careers as Analysts, Data Scientists, Data Engineers, and even Software Engineers, a unique career progression in this industry. For the already heavily technical professionals – this is a role that provides both engineering challenges and the chance to work close to the business. Wherever you are on your career journey, Analytics Engineer is a great opportunity from a career growth perspective and can help get you where you want to go. You’re no cog in the wheel here. As an Analytics Engineer, you can help drive decisions that make an impact for your company.

Analytics Engineers on Your Team Can Drive Value for Your Business

Though this position is relatively new in the grand scheme of technological advances to help drive business, it is in demand and growing exponentially. So, it’s important to know if you’re business needs someone to fill this role, you need to know what you’re looking for. For companies, whose main objective is making Data-driven decisions regarding customer retention, marketing campaign conversion, supply chain analytics, etc.

The role of the Analytics Engineer can be a perfect addition to both managing large amounts of Data coming into the businesses and helping drive value.

Take a look at our latest Analytics Engineer jobs here or get in touch with one of our expert consultants to find out more:

For our West Coast Team, contact us at (415) 614 – 4999 or send an email to sanfraninfo@harnham.com.

For our Arizona Team, contact us at (602) 562 7011 or send an email to phoenixinfo@harnham.com.

For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.

The post What’s Hot in NYC’s Data Market? Modern Analytics Engineering is on the Rise appeared first on Harnham.

Knowing how well, or not, your customers or service users interact and engage with a product is integral to the success of your business.

Whether it’s a bed from a furniture store or a button on a website, having the insight to understand how easy it is to use or how desirable it is amongst your customer base, then enables teams to go back, tweak the product and optimise it to its full potential.

This is where product analytics comes into its own. Those working within the field – product analysts – are integral in increasing conversion rates – whether that’s purchase rates or how user-friendly a product is – using a mixture of digital customer analytics and data science. From the NHS to Ikea, product analysts are highly sought after in nearly every industry as they strive to make their services and products the best they can possibly be.

What happens if work needs to be done on a product?

Initially, product analysts would undertake testing, such as AB testing, to decipher if there is a more favourable way of presenting the product or service to their customer base. They may also look at implementing tools such as personalisation, a newer capability on the market, to target their service to a specific user, making it more relevant and therefore able to boost conversion.

Once the product analysts have gathered any insights on what would optimise the tools, products, and services, these are then taken to stakeholders to kickstart the process of improvement. From here, updates are made by teams such as those in user experience (UX), and the product is re-launched and continually monitored.

The different arms of product analytics

Product Analytics, while seemingly a straightforward division of Data & Analytics, is extremely broad and split up into a multitude of sub-divisions. So, while all teams may be integral in spotting room for optimisation, their exact role will be different to another analyst.

For example, a trend analyst will analyse trends over a specific period, learning about those patterns and then optimising products or services for those times. Tesco, for instance, will be prepared to put the purchase button of turkey, pigs in blankets, and roasting potatoes at the front and centre of its website at Christmas.

Journey analysts however will measure where customers come from to engage with a product or service, be it a banner ad, an email, or a social media post. They’ll also look at where in the customer journey purchasers or users drop off, finding kinks in the service experience that need to be ironed out.

How to get into product analytics

Like the sound of what a product analyst does? Here’s how to work your way into the industry.

Most businesses will aim to hire individuals with an extremely proficient maths or statistics background; business analytics qualifications will also stand you in good stead as will data science. Additionally, you’ll need to showcase a good understanding of SQL – the tool most frequently used within the sector.

Degrees are no longer as important as they once were, especially in the current climate where there are more vacancies than skilled candidates. Many businesses are far more open to hiring potential employees who hold a few crucial skills and then upskilling them as they go, rather than finding the polished product.

However, the division doesn’t usually see graduate-level talent enter, it can take up to 18 months of work until candidates can think about becoming a product analyst. However, once you’re there you can expect a starting salary of £35,000+ and the opportunities to reach up to £120,000 per year.

Product Analytics is a relatively new division within data and analytics, but one that is gaining traction at rapid rates. By 2028, the area is predicted to be worth $16.69bn as it gains popularity across businesses worldwide, helping them to both streamline and optimise their products and services.

If you are interested in entering the world of product analytics, please speak to one of our team today or take a look at our vacancies here.

The post What is Product Analytics? appeared first on Harnham.

Your pet is your family member and as part of your pack, you want him or her to go on your adventures with you – boat ride, anyone? Hiking? You bet! But to have a healthy, happy, well-adjusted, and accepted pet into all these new frontiers for four-leggers, the pet industry is booming. If you’re a pet owner, you already know how to make your pet thrive in their environment, whatever that environment might be. But if you’re in the pet industry, there are a couple of well-known big box retailers who dominate the market, though small businesses are catching on and moving fast. Enter advanced analytics and what you might call pettech, to help the pet industry thrive.

So, what exactly are we segmenting and measuring in the pet industry?

Pet Industry Markets

The pet industry can be any number of services or products with more being created every day, but here are four goods and services.

• Pet food, treats, and supplements
• Pet care and services such as grooming, boarding, training, and of course, veterinary services. Not to mention pet insurance, purebred registrations, microchipping, and a variety of other services helping pet parents best care for their pet.
• Pet products and supplies such as bowls, collars, leashes, carriers, and pet and comfort toys just to name a few.
• Pets themselves – Designer dog? Rescued cat? Class hamster? Whether you rescued your pet, worked through a breeder, or fostered to adopt, there are specialized industries within these markets as well to help the professional help you, the pet owner.

But it is the first three with the most data gathered across industries. Using advanced analytics helps the pet industry thrive through segmentation, correlation, and association. Segmentation helps find similarities while correlation defines relationships, and association follows occurrences of ‘when this happens, that is the result.’

Pet Industry Trends to Watch

The pet care market is expected to reach $350 billion by 2027 up from $261 billion in 2022. As the pet industry continues to climb, advanced analytics can help the market thrive by going where the customers are. The ease and convenience of e-commerce, and the generational shift from baby boomer buyers to millennial buyers determines where pet industry markets should focus their efforts. Here are a few trends to watch:

More than Kibbles ‘n Bits, the Pet Food Market is Booming
The pet food market is $91.1 billion worldwide making it the fastest growing segment in the industry. Add to this natural pet foods, treats, Animal care, and pet supply sectors are especially ripe to increased demand. E-commerce is essential to success. Up and coming e-commerce brands or legacy brands that have shifted online are uniquely positioned to capture the market share of customers who prefer to do their shopping online versus in store.

Looking Fabulous in Pet Fashion
Pet retail. Not far behind food and services is pet fashion. Think life jackets for boat rides. Sweaters and boots for cold weather. Harnesses for walking and slings and carriers to carry your pet as they await their final vaccines making them ‘street legal’ and immunized to walk safely where other pets have gone before to protect them from disease.While there is no single source for Data about the Pet industry, there is enough to correlate and see the pet industry is booming and growing. Having a pet a couple of years ago, emptied pet shelters and left breeders scrambling to have enough to meet demand. Demand for pets may have leveled off for some breeds, but the pet industry is the newer kid in town when it comes to advances in technology, Advanced Analytics, and Data, and catching on quick.

If you’re interested in digital analytics, advanced analytics, data science, machine learning, or robotics just to name a few, Harnham may have a role for you. Check out our current advanced analytics jobs or contact one of our expert consultants to learn more.

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Thanks to its capabilities through technologies such artificial intelligence (AI) and machine learning (ML), sports data analytics has opened a wealth of opportunities to teams across all sports games to perfect their strategies and tactics, understand who their best players are and why, as well as analyse risk factors such as injuries.  

One sport which regularly uses sports data analytics is tennis, and with Wimbledon currently gripping the nation, there seemed like no better time to deep dive into the use of analytics in Britain’s most historic and most-loved game.

The History of Wimbledon

Wimbledon is the oldest tennis tournament in the world, with the first championship taking place nearly 150 years ago in 1877. While it was only Men’s Singles played at the inception of the game, a few years later in 1884, Ladies’ Singles and Men’s Doubles were introduced. The final matches, Ladies’ Doubles and Mixed Doubles, were introduced in 1917. Despite its long-term presence in the UK, its popularity has only grown – especially via television audiences. Indeed, the two-week tournament held in 2021 attracted a cumulative audience of 15.5m from the BBC’s coverage.

The History of Data at Wimbledon

Sport Data Analytics arrived at centre court in 1991 after more than a century of players and coaches simply needing to guess what was working and what wasn’t working. While the use of metric measurement emerged at a very basic level for tennis, the understanding and use of its abilities grew rapidly alongside the incredible advancements of available technology. In 2015, IBM – for the first time – shared rally data which consequently provided game-changing insights into the optimum way to practice through mathematical equation. Not only did this information allow for more efficient practice, but it ensured players weren’t being overworked unnecessarily for very little return.

What Else is Measured Through Tennis Data?

There are numerous data sets collected during tennis, both in matches and in practice. Some devices within a competitive match enable more accurate refereeing, such as supervision of balls landing on the lines, similar to technology such as VAR which is found on the football pitch, video replays, and fan engagement. During practices, players may opt to use wearable devices on their bodies and racquets so that coaches and teams can see where both their strengths and weaknesses lie throughout the game. This then enables the creation of tailored practice strategies to help them improve their performance.

Data Analysis within tennis can also give a good indication of patterns of performance, facilitating strategic gameplay from players. A good example of this can be found in this Towards Data Science article which explores the importance of the first serve in tennis and whether it can guarantee success in a game.

Not all players embrace data in tennis, however. According to tennis.com, Federer – while beginning to use data after his injury comeback – remains cautious of its value. Nadal embraces racquet sensors but chooses not to delve into the stats of the game at all. On the other side of the coin however, Djokovic was the first player to fully implement data insights into his game, employing a data analysis consultant as part of his team.

But whether you’re for the use of data in the game or not, there’s no denying that its value in the market is creeping up slowly but surely. We’re likely to see more adoption of its use over the coming years and Wimbledon 2022 is undoubtedly going to be fraught with insights before, during and after matches.

If you’re looking to build out your data team, or to take the next step in your career, we can help. Take a look at our latest data jobs or get in touch with one of our expert consultants to find out more.

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