WHITE

PAPERS

AND CASE STUDIES
Scribble Enrich Use Case 1:
Voter Data Platform for National Party

Enrich was used as the underlying platform for a major National Political Party to build detailed profiles of their voter base, with attributes such as address (approximated from multiple sources), leaning, age, issues close to their heart, among others, and to help build a high-touch campaign through channels such as text, whatsapp and social media.

Read the White Paper

Scribble Enrich Use Case 2:
Datalake Enrichment at a National Retail Chain

Scribble is working with India's largest brick-and-mortar retailer on their project to scale their growth 10x over the next three years. As part of this engagement, the Enrich platform computes attributes for a number of core entities (like store, customer, SKU) to continuously compute rich profiles for them at the granularity of each individual entity. This enables near real-time decision making on actions like assortment, new store locations, customer engagement through marketing and offers, among others. Read the White Paper

Boost insights from your data for an accelerated impact on your bottom line. 

Scribble configures the system not just to your domain, but to your specific business context.

Your data scientists have the prepped data they need, computed continuously. Your data gets the attention it deserves, and your bottom line is happy.

Scribble Enrich Use Case 3:
Shopping Paths at a National Mall Chain

The Enrich platform is used to help a national chain of malls understand the shopping paths and behaviours of shoppers by continuously ingesting WiFi data to compute attributes such as visit frequency, brand affinity, and shopping paths. This gives the mall various levers to both, enhance the individual shoppers' experience with customized offers, as well as attribute revenue to various such initiatives.  

Read the White Paper

ARTICLES

AND BLOG POSTS
Why your business doesn’t have to wait, to start giving back

“If you’re in the luckiest 1% of humanity, you owe it to the rest of humanity to think about the other 99%.” — Warren Buffett.

 W.B. has given away more than he has left. In fact, he has pledged to give 99% of his wealth. It gives us pause. When talk of CSR and philanthropy are brought up in most offices, there’s a general lull in the air — the process seems like an imposition for most who just consider it another thing to tick off a sundry list. But the act of giving is powerful.  Read more here.

How to turn your startup into a data-informed business

This post is a useful way to think about how to start on a data journey if you're a young startup that's just pushed data to the back burner (say until you had 'enough' traction) or even if you're part of a more mature company that's used to making decisions more on instinct and experience, but now want to complement it by building data capabilities.

Read it here.

Reducing Organizational Data Costs

We speak to a number of organizations who are in the process of building and deploying data infrastructure and analytical processes. Organizations face a number of challenges that prevent them from meeting their analytical business objectives. The idea of this note is to share our thoughts on one specific challenge - high cost. Specifically: 1. Cost model - Deconstruction of cost, 2. Drivers - Drivers of each cost dimension, 3.Recommendations - Actions to address each driver

Read more here.

How to get the most out of your organization’s data: The mindset

Every business is a data business And while this aphorism has been around for some time, what does this actually mean to enterprise stakeholders? What should key decision makers be valuing and excited about as they start to invest in analytics tools and ML/AI?

Here’s what we think are the most important aspects to embrace when it comes to data and enterprise.

Read more here.

Should data scientists be excited or worried about the new privacy laws?

The General Data Protection Regulation (GDPR) legislated and passed by the European Union has sent ripples around the world, and depending on who you ask, this could either spell apocalypse, the workings of a nanny state, or a very positive step towards consumer privacy. 

Read more here.

The Metadata Economy - The Future of Trusted Data Sharing

This post talks about symbiotic businesses buying and selling their data from and to each other, like an ecommerce business with a property developer, unearthing new areas in the city with higher disposable incomes to ramp up their delivery capacity in those areas, in the case of the former, and consider fancier apartment construction for the latter. The businesses participating can be orthogonal in the markets they target, in the industries they’re in, and even their revenue models, but the key to building value is in organizations discovering the right, trusted external data to grow their business. Read it here.

The Pitfalls of Data Science (and how you can avoid them)

Depending on who you ask, you’re going to hear data science described as being sexy by some, and decidedly not so by others.

Sexy, I suspect, because in today's geekdom-loving world, we imagine the lab coats have finally turned their laser-like, academic precision to the final economic frontier, data, and the dam holding back all those dollar-laden insights from that data is about to burst.

Read more here.

How to Architect for Data Consumption

This is my pet peeve - technical architects are building systems and applications that make data analysis complicated, error prone, and inefficient. We need enablement of data consumption as a first class requirement of any system that is built. I explain here how we could architect differently.

Technical systems architects, including myself until recently, are used to building systems with considerations such as development time, robustness, and evolvability. Any analytics was an after thought. Having spent a few years crunching data at various scales, I see the world differently. I see barriers all around in systems to analytics. Here are a few thoughts on the nature of barriers and how to address them.  Read more here.

What Can We Do With Meta Data?

As the complexity of data and systems that hold data grows, the cost of analysis increases due to time and effort spent in figuring out the feasibility, appropriateness, access, and management of data. We believe that a number of new low-risk and valuable applications can be built through creative application of metadata that can help cope with growing complexity and reduce cost of analysis.

Our pure metadata-based cloud product, Scribble Assist, is the first of many applications that will be built by the larger community. Our experience with customers has convinced us of the value of the approach and that a lot more will come. We discuss how we see the landscape of metadata applications. Read more here.

Data Shifts Power Within Organizations

A major challenge in going more data-driven in organization has less to do with data itself, and more to do with the ability to manage the dynamics that emerge as decision makers look at data as an input to decision process.

I have a particular kind of power shift in mind. I am not referring to the process of democratization of data, which we will discuss in a future article, but rather one due to the emergence of a new powerful organizational entity, The Scorer. The Scorer derives its power from data and its ability to judge both past and future decisions. The Scorer provides alternative decision paths taking away some agency and control from existing decision makers. In this article I explain why and how this happens, and how the process could be managed. Read it here.

Available but Unusable Data - 2 : Semantic Gaps

At Scribble Data we are thinking deeply about why decision makers are not able to get to the data when they need even when relevant data is available in their own databases. The reason this question matters is because we find that decision makers routinely make high risk decisions involving products, marketing, and operations with very limited and ambiguous data, and the absolute cost of an incorrect decision, or the opportunity cost of a delayed decision is significant. In my previous article, I elaborated on why available data is unusable. There are systems and organizational issues, and hard technical problems. The focus of this article is the core technical problem of semantic gaps - the gap in the meanings of the question and the data. Read it here.

Available but Unusable Data: Emerging Organizational Challenge

This post was motivated by a recent article on inability of organizations to apply data. This is something I am deeply thinking about these days. In this and future articles, I wish to explain in simple English what is happening, why, and what to expect over time.

Read it here.

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