Would you like to see a Demo of Psithur Connect or discuss the technology further?


Interested?

When I set-out to build Psithur Connect, I wanted to build a product that delighted its users and made them feel as if there was a little bit of magic at their fingertips. 

#### Co-founding Psithur

I never studied computer science; my formal education is in Economics where I learned the concepts of game-theory, behavioural economics, cost benefit analysis and more differential maths and matrix algebra than I’d care to admit. While I no longer work in the field, my Economics degree continues to serve me: Economics is a useful framework for thinking about the world and complex systems: and software engineering certainly requires a healthy dose of cost-benefit analysis and systems thinking!  

Everything I’ve learned in software engineering, I’ve learned through trial and error, a fair amount of luck and from listening to the wisdom of others. I hope to never stop learning, but like anything, learning needs to be practised.

I started writing software 20 years ago in high school - where I taught myself C++. Since then, I’ve continued to learn new programming languages while working as a research economist, data scientist, founder, data engineer, software architect and software engineer. 

The longer I spend working on technical and data-focussed systems the more I appreciate the perspective gained from having a sense of the costs and benefits of various technologies and technology choices. 

Psithur is the continuation of many years of learning how to navigate these trade-offs in order to achieve systems that deliver real value and help organisations solve real problems.

#### Gardening

My favourite analogy for software development is gardening: they share many similarities. 

A landscape gardener is like a software architect; she takes her client’s desire for a beautiful and functional garden with various features. Using her knowledge of the climate and the soil, she examines the landscape and selects and sites suitable plants. Some gardens might need a lot of preparatory work, some only a little, but chances are once the landscape gardener is paid and gone, the garden is anything but complete. 

Aside from regular weeding, watering and pruning, over time the garden will change. Things that might have seemed neat and tidy will grow and become messy. Plants may die, some will flourish out of control. If the garden is not maintained it becomes wild and loses its function. Cared for - however - a garden improves and may have new function over time. Gardening without the right tools is ineffective and inefficient. A gardener caring for a garden needs patience, stamina, perseverance and perspective. Reflection is as important as action.

I started working on Psithur Connect (at the time called *Symplast*) in August 2020, and I wasn't confident that I could build something to match the vision Jason and I shared. I hadn’t built something of this scale or complexity with Clojure before and I didn’t know if it would work. Eighteen months later though, I can say that the foundations are solid and much trial and error has paid off. 

Psithur now has a product that we think is exceptional, and although there's still much we want to build, I wanted to share some reflections from the last 18 months and try to articulate why Psithur Connect’s foundations help make it an exceptional product. 

#### Clojure

I was introduced to Clojure a decade ago by a colleague. Specifically, [a video by Richard Hickey (Clojure's creator) titled "Simple Made Easy"](https://youtu.be/SxdOUGdseq4). In this video, Hickey introduces some of the concepts that drove him to create a new programming language. While I didn't start using Clojure properly for another 5 years, I recognised the wisdom in Hickey's arguments about software and complexity and I was drawn to learn more. 

Exploring more of Hickey's work led me to think differently about programming. I started changing the way I wrote Python and adopted some of the functional paradigms at the core of Clojure (such as immutable data). I was lucky at the time to have a boss who was happy for me to learn Python on the job. Over 5 years of writing lots of Python and JavaScript with a slowly growing unease at what I couldn't do in these languages, I started using Clojure properly.

In hindsight, my unease was a manifestation of the lack of expressiveness of the languages I was using; that is, the measure of how much work it is to express a particular idea (or rather, how much code you need to write to solve a particular problem). 

About five years ago I started using Clojure properly, albeit for hobby projects. It was a steep learning curve, but the more I used it (and the more I learned how to use it), the more I recognized its value. 

I won't write more about [Clojure](https://clojure.org/about/history) itself here, but rather I'll point you to a blog post by Gene Kim, whose book [The Phoenix Project](https://itrevolution.com/the-phoenix-project/) should be on every project manager's shelf, "[Love Letter to Clojure](https://itrevolution.com/love-letter-to-clojure-part-1/)".

#### Graph

Over my career, almost every data problem I’ve worked on has had an aspect of Graph: more often than not, I didn't have access to the tools to apply a Graph-based solution.

One example that comes to mind; over a decade ago, working as a Data Miner at a large Australian Government Department, I was tasked with improving fraud detection algorithms within a category of transactions the Department was responsible for processing. The Department had invested in an Enterprise Data Warehouse - which was very good at storing very large tables of data. 

This Warehouse was also reasonably good at answering simple questions such as "find all the transactions that occurred from accounts with the same email address and order them by quantity". The problem was that the patterns of fraud weren't limited to one attribute. That is, the question needed to be generalised to "discover every group of transactions where the account linked to each transaction shares at least one attribute with another account and order the results by the total number of accounts in that group".  

You might recognise this as a classical Graph Analytics problem (social network analysis). My crude SQL solution using stored procedures and set theory was actually in the end very successful at finding "interesting" networks, but the large Enterprise Data Warehouse was not the right tool for the job.

Today, if asked, I would define a Graph enabled system as a system that has the ability to navigate arbitrary relationships in the system's data: a capability that boils down to two things: the programmatic interface to the database and the choices made by the database in indexing its data. While SQL [is good for some things](https://www.scattered-thoughts.net/writing/against-sql/), it's not suitable as an interface to data that is structured as a Graph. 

Psithur Connect is built with Graph capabilities at its core, to be able to navigate arbitrary relationships (nearly) instantly, both in the algorithms and business logic running on the backend, as well as the user interface on the frontend.  

#### ClojureScript

As a data scientist and data engineer, I spent a lot of time writing server-side Python to manipulate data. While this type of effort is a significant and necessary part of any data-related project, I always felt the options for non-technical users to derive value and insight from their data was limited. This was because the languages that I was using were good for manipulating data but of no use to almost anyone else. 

What was missing was expressive, well-designed, performant user interfaces where data could be brought to life, be explored, triaged and shared, and most importantly where insights were accessible, not just to those people who could code.

In my last startup, I built bespoke full-stack analytics and business productivity platforms using Python and JavaScript. While I was able to craft some [really neat user experiences](https://www.trinityp3.com/2015/02/calibr8or-calibrates-media-agencies/), again, the tools I was using didn’t match the ambition I had. 

One of Clojure's super-powers is that it works both on the server (or backend) for data processing *as well as* the frontend, for user interfaces. ClojureScript is the Clojure variant that compiles to JavaScript and hence runs in a web-browser. This means that a project (like Psithur Connect) that uses Clojure can share code and business logic between the frontend and backend and, one developer, working in the same programming language, can build both. 

This reduces complexity (no more translating data or models between two disjoint systems) and enables a single system that not only feels coherent to develop but, far more vitally, feels coherent to its users.

#### [XTDB](https://xtdb.com/)

We've built our backend tightly integrated with XTDB, a database that offers a unique combination of features that no-other database has :-

*   A schemaless, document oriented model (such as MongoDB)

*   Full change history (MarkLogic, Datomic, countless retrofitted EDWs)

*   Graph traversal (such as TigerGraph and Neo4j).

*   Full text search (actually uses Lucene, such as ElasticSearch)

*   A datalog interface and [pull syntax support](https://edn-query-language.org/eql/1.0.0/what-is-eql.html) (Datomic)

In addition to those features, XTDB is built with Clojure and is OpenSource and extensible through a modular (aka “unbundled”) architecture - in-fact we're using three custom developed XTDB modules currently.

We didn't start building with XTDB though. Psithur Connect - as it is today - has undergone many rounds of innovation, of trial and error, of improvement, resulting in a system built from many components that are integrated together to create a coherent whole. 

#### Datomic

A little while into my journey to Clojure, I came across another of Richard Hickey's projects - [Datomic](https://www.datomic.com/). If you'll accept my assertion that Clojure was a revelation to me as a Python programmer, you should also believe me when I say that [learning about Datomic](https://tonsky.me/blog/unofficial-guide-to-datomic-internals/) was a revelation as a Data Engineer. 

For many years, I'd been thinking in terms of data locality, indexes, schemas and ontologies, audit and history logging. Datomic is a database built on the same concepts as Clojure and it's designed for a certain domain of problems - core business information systems - which are - in their essence - models of real world relationships. It is opinionated in its design (and unapologetic). But the revelation was the possibility of a database that can solve a unique set of intersecting requirements, specifically, operating as both a graph database and relational database while maintaining a complete history of facts over time.

While we have now migrated our platform from Datomic to XTDB, we owe a large debt to the work that the Datomic team put into their product. The ideas Datomic embodies have been critical to the design of Psithur Connect. 

#### Clojure and Psithur Connect

Clojure is **the** reason we've been able to build an application like Psithur Connect. 

Clojure is expressive so complex ideas can be expressed tersely (which aids simplicity) and complex problems can be solved iteratively without rewriting large parts of the whole. 

Clojure is extensible, so we can leverage the best open-source libraries; not only from the Clojure and ClojureScript ecosystem, but also the Java and JavaScript ecosystem. 

As if that was not enough, in the last few months we have also integrated machine learning libraries into our product using the [JVM's Project Panama](https://openjdk.java.net/projects/panama/) which extends further our integration options to include native C implementations of popular machine learning and vector search libraries. These are now integrated into the core of Psithur Connect.

As a developer, working with Clojure has spoiled me - for now I know what it's possible to achieve when you have the right tools for the job.

As a company, we're excited to bring Psithur Connect to more customers and while we very rarely talk about the technology that it is built on (because it's actually irrelevant to almost everyone), I wanted to at least pay some homage to the foundations that Clojure has provided for us to build a software product and company where we can deliver a rock solid user experience that delights our customers and maybe even feels a little bit magic.


Building Psithur Connect

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#### [**View the slides**](/assets/Psithur-Think-Piece-Jan-2022-Slides.pdf)

# **Transcript**

### Introduction

Hi and welcome to this Psithur ThinkPiece where you’ll learn how graph technologies will revolutionise the way CRM users’ leverage data.

I’m Jason, co-founder of Australian startup Psithur.

Psithur is a graph technology company helping people get more out of their hard-earned prospect and customer data. We’re all about helping prospecting and sales teams find the gems in their CRM databases to generate more effective asks.

For those who may be new to this field, Customer Relationship Management databases, shortened to CRMs, are a common way to capture and manage information relating to their customers and leads. Some leading CRM vendors you may have heard of include Salesforce, Hubspot and Blackbaud.

This webinar focusses on what data-led prospecting will look like in the 2025 timeframe. We believe there are three enabling elements converging that will turbocharge the way that fundraising and sales teams use their CRM databases to build their pipelines. You’ll hear about these three convergent elements and why we think they should be on your and your team’s radar.

Let’s get started!

### About Psithur

Under the hood at Psithur - we’re a graph technology company. Since founding in 2021 we’ve solved the hardest graph technical challenges - meaning we bring the best of graph-technology in an easy to deploy, use and manage product. Our customers, and their IT teams, don’t have time to become graph technology experts, and they don’t have to - our technology opens the door for them to leverage graph while staying focussed on their jobs - that is finding and engaging the best leads!

Our product is built on solid foundations and open source technology, meaning that we have full-control of our technology stack. This ultimately benefits our customers through more adaptability and more options for tailoring to their specific needs.

Although graph technology is what we know best, we are driven by helping prospect research, development and sales teams find and engage the people they need to achieve their goals. We are completely invested in your respective missions, whether that be finding the next major-gift donor for a cancer research scholarship, or your next enterprise customer who is looking for your product but doesn’t know it yet. We aim to revolutionise the way prospect research and sales teams leverage data to grow their pipelines.

Felix and I founded Psithur after spending the past decade working in data and IT projects with the Australian Federal Government. We saw how vibrant and accessible data solutions can be truly transformational for non-technical users. We were so excited to see users with no data science background wrangling complex datasets to find the nuanced insights they needed. We wanted to bring this experience to the prospect research and sales worlds - two worlds that focus on scaling human connections to bring about positive change.

### Our Team

Speaking of our team..

Felix is the brains behind Psithur’s graph technologies. He has  overcome many significant technical challenges to make our product market ready.

Felix has a deep and wide-ranging background in data engineering and full-stack software development. Psithur is Felix’s second startup so he brings established business operating skills and know-how. Felix cares about making better software that from the user’s perspective gets the basics right.

Kat is a sales and marketing leader with wide-ranging operating experience in software startups. Kat has developed valuable real-world *insights* into the challenges and needs of Sales Development Reps and Account Executives (the equivalent of prospect researchers and development professionals in the fundraising world). Kat is driven to grow meaningful connections with customers and seeing them succeed.

And I’m Jason - I lead Psithur’s business and operations side - I bring over a decade of experience working at the interface between researchers, developers and users. Most recently I led teams of business analysts playing a critical role gathering and documenting user perspectives and requirements. I get a real kick out of advocating for user perspectives through the tech development cycle.

### Wanting more from your CRM?

Through our engagements with prospecting and sales teams over the past 6-months (we’ve had over 100 conversations!) we were super encouraged to see that the vast majority aspire to better leverage data. Many years and dollars have been spent building out, and modernising their CRM databases - expectations are rightly high! We’ve heard though that many feel there is something still missing.

A core function of any prospecting and sales team is to feed their pipelines with new leads. From our chats with these teams with we’ve learned there are some data-driven insights that would be truly game-changing, such as;

*   Who are the known leads that should be on my priority list? These are the leads that might already be in your CRM database but due to data overload remain hidden.

*   Who are the *unknown* leads that should be on my priority list? These are the leads that aren’t even in your CRM database, but really should be!

*   Who are the influential champions aligned with my cause? These could be recommenders or public advocators, and should be nurtured.

*   Which leads meet or don’t meet my due-diligence criteria, right now or at any point in the future?

Another core function of any prospecting and sales team is lead conversion - once I have found and qualified a lead how might we best secure the pledge or commitment to buy? We’ve learned that some data-driven insights that would be game-changing are;

*   Who in my network could arrange a trusted, warm-introduction to a high-priority, hard to reach lead, for example an Ultra High-Net-Worth individual, Chief Information Officer or a celebrity?

*   How can I personalise my outreach? And how do I do this at a greater scale? What actually matters to my lead?

*   When and how should I connect with my leads? Are they primed to be asked to give/buy?

Some teams we’ve spoken to have begun dabbling with data and technology attempting to address some of these challenges BUT we’ve learned that nearly all teams face significant hurdles to gaining the valuable insights they need from their CRM databases. Business Intelligence reports, although useful for reporting metrics simply aren’t providing the nuanced, granular insights front-line development and sales professionals need.

We’ve heard from many in the industry that it feels like there is so much data available but so little time to make sense of things. As a consequence, teams often find it hard to be proactive and strategic with their prospecting - with a lot of tasking being reactive in nature and heavily focussed on wealth-screening.

We have also experienced these same frustrations and challenges. Kat, our VP of Sales and Marketing comes from the software sales universe and has lived the frustrations of data overload. Over and over again Kat has seen companies that have 10k, 20k, 30k leads in their CRM database but they still don’t know where to begin.

It’s no wonder prospect research and sales professionals want more from their CRM data! The secret here, and this is a key takeaway from this ThoughtPiece - **the secret is that the problem is *not* your CRM database**! Most CRMs are fantastic in what they do - serving as a system of record and management. But, we feel this is akin to paying for a 4K-high-definition streaming subscription but only running on a dial-up internet connection. There is no way you could possibly enjoy the full viewing experience you’re paying for!

At Psithur we believe that graph technology will play a key role in helping CRM users get more valuable insights from their CRM databases.

**Graph has arrived!**

It is becoming increasingly evident that graph technology really has arrived in the market. In fact, you’re likely to already be using services built upon graph technologies. Examples include Google search engine, LinkedIn connection recommendations, UberEats meal recommendations and Gmail’s autocomplete feature.

Some really interesting applications of graph technologies can be seen in financial fraud investigations, IT security breach analysis and optimisation of manufacturing processes.

Leading technology market research anticipates that by 2025, only 3-yrs down the track, graph technologies will underpin the majority of data and analytics innovations.

Behind the scenes there is feverish innovation and development occurring in the startup world. A new generation of firms are building business-tailored solutions using this new technology, Psithur obviously being one and we’re really excited to be an active participant during this time.

### **Basic graph principles**

So before we go any further - you might be wondering what graph technology actually is - and what it’s comprised of, and how does it work?

At its most basic level, graph technology is a way of storing highly connected data that makes it easier to derive insights that wouldn’t be possible otherwise. By highly connected I mean data that contains ‘one-to-many’ characteristics - for example, one person in your database has many known attributes or associations. CRM databases are a fantastic example!

Graphs are built upon Graph Databases. Graph databases are unique because they assign equal value to both the data node (ie. people, places, things) and the relationships or connections between data nodes (ie. knows, is a member of, participant in an activities etc.).

Knowledge-Graphs are a specific application of graph technology. Knowledge-graphs map and visualise the relationships between data and provide information that helps humans and machines understand what the data actually means - this is also called a ‘Semantic-Layer’.

\[SLIDE]

Here’s a somewhat trivial example of a knowledge-graph..

Graph analytics are the algorithms used to automatically derive hidden meaning and insights in knowledge-graphs using statistical techniques. Taking graph analytics to the next level is Graph Artificial Intelligence (AI) which are analytics that can be built in an unsupervised fashion.

### 2025 vision for prospecting and sales data ecosystem

Reflecting on the challenge of how to get more out of your CRM - at Psithur, we believe that by 2025 three key elements will converge in a seamless, dynamic ecosystem; data, analytics and graph.

The headline, and exciting news is that this will revolutionise the way that CRM users interact with prospect and customer data.

The next generation of prospects and customers will be EXPECTING that they are treated as individuals, with unique interests, passions and motivations. The *only* way to do this at scale is to leverage technology and data in new ways, and we believe that adoption of graph technologies will be critical.

### Vision for data-driven prospecting in 2025

Now I know there’s a fair bit on this slide - let’s step through each of these three elements one by one..

Starting with data

*   In 2025, your in-house CRM data will still be your ‘crown-jewels’ - it is however even more valuable through enrichment with third-party data. This will enable a more up-to-date and holistic view of a prospect.

*   A key point here is that by 2025 you’ll be able to tap-into a rich ecosystem of third-party data providers - you’ll be able to switch them on/off as you need, only paying for what you use and leveraging the economies of scale that come with wider adoption.

*   By 2025, Natural Language Processing (NLP), which is the automated comprehension of free-text - ie. the meeting notes captured in your CRM will help you find the key bits of information you need without have to read back through potentially years of messy notetaking!

Moving to analytics;

*   I call the Business Intelligence (BI)I++ because BI is just the start

*   Visualisation using knowledge-graphs will enable you to immerse yourself in a dynamic Relationship Map of your prospects and customers, not quite Minority Report style but you get my drift! You’ll be able to quickly get a ‘360-degree’ view of your lead without reading through endless tables and slabs of text, you’ll see pathways for warm-introductions to new leads and you’ll see highly connected or influential prospects stand out.

*   Graph Analytics will help build automated predictions about propensity to donate/buy so you can focus your teams efforts on the best leads. Graph Analytics will also allow you to see people who are similar some of your best customers - think of this as being like a meal recommendation from your food-delivery app - if you liked the chicken curry you might also enjoy the new goat-curry!

*   AI, Artificial Intelligence will enable analytics at scale, unsupervised - almost as your personal research assistant

*   Like with third-party data, by 2025 there will be a rich ecosystem of third-party analytics providers - you won’t need to build these in-house! You’ll be able to buy, build or rent tailored analytics that suit your needs, when you need them!

And finally to graph;

*   We believe that graph will be the underlying enabling technology to make this dynamic ecosystem a reality - helping you find and engage the gems in your CRM database.

Alright, let’s take a brief pause - we’ve seen how users want more from their CRM databases - we’ve seen how Graph technologies are emerging and will play an increasingly important role in helping prospect research and sales team get more from their CRM databases.

What might that actually look and feel like to a user? And what does that mean for my team?

### How will graph help me?

Well, what its looks like is going from tables to immersive dynamic knowledge-graphs. Top right is a screen capture of a simple demo in our Psithur graph platform. The dots, or nodes represent people or organisations and the lines represent relationships or associations. You can see people and organisations linked and we’re already seeing that there are two, previously unknown people who may know or may have similar interests to your current customer Ramon.

By using graph technologies in conjunction with your CRM database..

*   You will see your data come to life, seamlessly integrated in a single window

*   You will be able to see and track opportunities to purchase third-party data, without having to jump across to a different tab and log-in to your third-party accounts

*   And with a bit more development, you could even receive recommendations when the analytics engines see opportunities you don’t

What this means for prospecting and sales teams is;

*   You will amplify the value of your hard earned CRM and customer data, deepen your understanding of your prospects and in doing so find those opportunities to grow your prospect pipeline. At the end of the day, graph can help you land more successful asks!

### Where do I start?

The next logical question is - where do I start?

The simple answer is, there are plenty of low-risk, low-cost things you can be doing right now to set your team up for success. We’d recommend a five-step process to building data-driven prospecting capability that delivers early value for your organisation.

NOW

*   Campaign pipeline strategy - what’s important to your organisation and how does data support that?

*   ID a strategy-aligned core-business problem to address through a pilot of graph technologies - at this point you should be thinking about time- and scope-

Data Quality

*   What data might I need to deliver a successful POC or trial?

*   What data quality issues do I need to address first? Duplicate records, Incomplete Records, Different Spelling and use of Acronyms.

Visualisation (Knowledge-Graph) - so now imagine you’re fundraising officer at a major Australian University - you’ve got business support to run a small-scale POC using graph-technologies to test it out. And you’ve identified and addressed the limited dataset needed and its quality.

The next step is Knowledge-graphs to visualise the relationships in your data. You have the data available to know that two individuals worked together at the same time - there’s a pretty good chance they know each other!

Enrichment - To step this up to the next level - enrichment.

Graph analytics - by this stage you’ll have a powerful new way to interact with your CRM data via the knowledge-graph, and you have confidence that your data is the most up-to-date and accurate due to third-party enrichment. You know who to talk to and who you can ask for a warm introduction. But now you’d like to scale this up and this is where graph-analytics and AI come in handy.

We know prospecting and sales teams are super busy - we also know that many probably feel quite fatigued by their most recent CRM modernisation push. This five-step process will make sure you can continue delivering on your core business while preparing for the future with graph.

### Key takeaways

In our opinion these are the key takeaways;

*   Your CRM database is great, and you *probably* don’t need a new one - at Psithur, we think you simply need a new way of looking at it!

*   Graph will make interacting with your CRM database go from feeling like its running over dial-up internet connection to lightning fast high-definition! Graph makes prospecting fun!

*   Graph gives you a way to find those nuanced leads and hidden opportunities that wouldn’t have been possible otherwise

*   Graph is already delivering real-world value to businesses and will soon be mainstream

Like the leading market research firms we believe that the adoption of graph technologies is inevitable. We think CRM users can look forward to a fantastic generation of new graph technologies that will bring their data to life. Early adopters will have a leg-up and will be in a powerful position to leverage emerging graph technologies as they are brought to market.

### Thanks and wrap-up

A huge thanks for your interest in learning more about graph technologies for prospecting and sales teams.

Feel free to [reach out](/contact-us) via email, <hello@psithur.com> to arrange a chat about your specific data challenges or to see a short live demo of Psithur’s graph platform for prospect researchers and sales professionals.

Thanks!


Resources

Building Psithur Connect

Psithur Thinkpiece: Are you overlooking valuable prospects in your CRM?