When you should work with us
You’re big. But you know you can be bigger.
You’re growing. But you know you can grow faster.
You know that without delving deep into your data and understanding it, you growth will remain linear.
1. Data scattered over a wide number of platforms, from many sources
Data originating from different sources is usually of different types. This data diversity requires additional transformations before analytics can be applied, making the process inefficient and costly.
2. Lack and expense of talent
(A game changing) Methodology needs top talent and is hard to define and validate. A single data scientist can’t do everything on their own, you need a diverse team and expert leadership. And most of the time this talent is prohibitively expensive.
3. Companies want to keep data science in-house
However, the large part of data science is better done by specialists who can create and do a straightforward handover to a mid tier python developer to handle.
4. Reporting to stakeholders is difficult
You don’t want to be focusing on how to create reports for your stakeholders and VC’s when you have a product to build and customers to attract.
5. Assumptions that aren’t always validated by data
This can lead to guesswork often resulting in expensive and/or irreversible mistakes.
How we work
The focus of data engineering is to extract, aggregate, and structurize often unstructured data from various disparate sources that your employees commonly use on a daily basis.
|The groundwork for all data science projects.
We extract (scrape), transform (preprocess and filter), and load the data into the data warehouse which is most appropriate for your case.
Why you need this: Your data is not in the right shape or format to be processed.
|We clean the data, create relationships, aggregate and choose the most suitable layout for data points. In this process we shape the data into the appropriate structure that is best for analysis and optimal processing or querying.
Why you need this: Fitting your data to data models, we are able to apply processing methodologies to it.
Data analytics offers diagnostic insights into business performance. This gives in depth understanding of the key root causes behind behaviour combined with business intelligence.
|KPI measurements allow business decision makers to shed light onto their business’s performance. Here we identify, while directly communicating with your decision makers, the KPI’s most relevant to your business.
Once KPIs are defined, our team designs methodologies to process the data and extract the KPIs. Most startups have similar challenges and similar KPIs that need to be extracted, relating to customer acquisition and retention, profit margins, conversion rates, activation and net burn rates, gross merchandise value, lifetime customer value, churn, or whatever is most relevant to you.
Why you need this: You need an accessible, high level overview of your business.
|In marketing analytics, event tracking is a flexible activity that tracks individual customers interactions with your platform. We setup selected event tracking tools to bring another level of insights in to customer behaviour and psychology to the decision makers.
Why you need this: To observe users’ behaviour and be able to setup meaningful user testing.
Data science uses predictive modelling to assess your future trends and can identify key factors to reach a desired state in the future.
|Predictive Business Analytics|
|We can use both large and small data sets to define data models and use them to predict the future. Over time, we feed these models with more data in order to improve estimates. This allows us to find trends and identify patterns, even within large sets of foggy data. This type of data science is particularly relevant for businesses that need to define newly emerging markets and their trends.
Why you need this: To be able to forecast and act on customer trends and use that to acquire / retain them.
|Predictive customer analytics and churn|
|Here we can predict what the customer wants, who they are, and why they like a particular product.
Customer churn is among the most important indicators for SaaS startups, especially when trying to identify individuals on the brink of churning (cancelling). To prevent churning, we identify the indicators relevant to churn. Then we develop methodologies for the detection of changes in these indicators. Finally, we define and train a model to predict how your customers will behave in the future.
Why you need this: Because gaining customers isn’t always the most difficult part, but keeping them is.
Business intelligence is assessing the results from data analysis providing understanding to why something is happening and transforming the knowledge into actions directly applicable to your decision making process.
|Business insights analysis|
|Data analytics and data science will generate appropriate KPIs and insights into the data providing what is happening in your business, which will then serve the business intelligence team to provide answers to why something is happening. Understanding reasons behind the growing or declining trends observed in the KPIs, or predictive models indicating surpassing warning thresholds in the near future is crucial for decision making to guide the business towards stability and success.
Why you need this: Understanding accurately the root causes for any trends will enable you to recreate growth where it is happening and find ways to mitigate any decline.
|As we work with you on the previous processes, a data visualisation expert will work with us to make sure that your data tells a story by presenting it in the most readable and approachable way. By visualising data, we can help you spot patterns, trends and correlations, allowing you to see the bigger picture.
Why you need this: Large datasets are impossible to comprehend for humans and can require supercomputers to find patterns. But with the right Business intelligence we can find out the best data to work with, and the best way to visualise and comprehend.
2 Week Sprints – Cancel anytime!
Why 2 week sprints?
Because it is just enough to get you deliverables while minimising risk.
What do I get out of a typical sprint?
You get an actionable insight, how big it is depends on your requirements and the complexity. But we never start a sprint before clearly defining all deliverables.
How many sprints will I need?
We can tell you an estimate based on our first call. A typical small project would require 3-4 sprints, and in some cases it can be less.
Do I need data science?
Yes. But that is the wrong question to ask.
Ok rephrase. How do I know how much I should invest in data science?
It depends how crucial it is to your business. But generally, a reasonable average spend for a company in the last few years is 2-5% of their annual budget.
How many people do I need in the company to operate and maintain the data products?
Typically a mid-tier developer should be able to handle any maintenance work and update without it taking all their time.
Who is the main user of the data products?
C level team members use data outputs to drive high level strategic level decisions, but it is best exploited when used by everyone in the team from business development managers to marketers.
What tools, stacks, and languages do you use?
Where are you located?
We are based in London next to Old Street Roundabout, but we have worked remotely with companies all over Europe.
I’m sold. When can we start?
As soon as we can get things sorted. Depending on the project, can be a couple of weeks, but sometimes we are booked for months.
Dr Chris BrauerCo-Founder & Chairman
Chris’s life blood is startups, he’s worked with tens, mentored hundreds, and taught thousands. He is the director of Innovation for the University of London Group, and has done investor due diligence on thousands of startups and made investment decisions on some of the best ones for Edelman. A keen cyclist, piano player, and won’t be able to leave london because he can’t be too far from the theatre.
Marton GasparCo- Founder & CEO
Marton’s passion is analysing businesses, concepts and brands, and keeping the team happy…he is the link between the business value and the team. Data Science expert with experience in Artificial Intelligence and emerging technologies, worked on a number of high impact projects for Fortune 500 companies including, Mindshare, IBM, RBS, OMD, Edelman and HSBC. If you talk to Marton for long enough, you will be sure to end up in a fiery discussion about psychology.
Isam UraiqatCo-Founder & CPO
Isam is a concept machine and our secret weapon to make your project shine. He often challenges the whole team with his abstract ideas creating the highest possible value out of the projects he touches.
Imola GasparAudience and social media analyst
Imola’s passion lies in data driven innovation. As the ex social media manager for Coca-cola, Vichy and La Roche Posay she has a deep understanding of audience data and consumer behaviour. On a project in Hungary, one of her posts was seen by 50% of the people on Facebook without advertising spend. She also made a Pug famous on Facebook.
Jan LikarApplication developer and integration specialist
Don’t be fooled by Jan’s age – he’s finishing a double major in mathematics and computer science and he has been coding since he was 14. He is an experienced Python developer interested in functional programming and cryptography, has extensive skills coding with web application frameworks, API development and application integration. Jan builds robots when he’s bored.
Jiaxin XieData Analyst and Business Intelligence expert
With an entrepreneur mindset, Jiaxin is passionate about solving business problems especially in the world full of uncertainties. Jiaxin has an MSc in Finance, obtained certificates in Strategic Business Analytics and Marketing Analytics, and Operations and Customers Analytics afterwards. She has been working as a senior consultant in an international financial technology company and has expertise in strategic business management, digital marketing analysis and financial management. Jiaxin is a foodie, and seems to be able to eat up to 3 times her body weight.
Jose BlancoData scientist / Machine learning expert
Working as a data scientist is like solving a never-ending puzzle, and every different approach leads to a new insight, to a new answer. What gets him up in the morning is the thrill of discovering unexpected patterns and seeing a machine learning model working like a fine-tuned machine“ Jose is a Data Scientist with expertise in automating processes. Over the years he matured in Python programming and is skilled with data modeling and working with relational databases. Jose’s skills are in statistical data analysis and machine learning. Want to have a long conversation about ice? Wondering how many turns it take to make an Old Fashioned? Jose’s your guy – a keen mixologist, and can make a conversation even about ice interesting.
Alberto MoralesData Engineer and Data warehouse expert
Balazs HuszarGraphic Designer
Balazs designs just about anything, 2D, 3D, industrial design, sculpture – he visualises the input you give him in 4 ways and then proposes 10 solutions on the day. Balazs has worked on various design projects including TED and helps your data to tell your story. When he isn’t taking pictures he is writing about rifles.