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AnalyticsGate 365 Partnership

AnalyticsGate 365 Partnership

We are very happy to announce our partnership with akquinet – AnalyticsGate!

 

AnalyticsGate with AnalyticsGate 365 product is an add-in for Microsoft ® Excel and Qlik Sense. AnalyticsGate 365, unleashes the power of the Qlik Sense ® platform directly from Excel. The Excel Add-In combines the advantages of Qlik Sense (data access, security etc.) with the familiar Excel working environment.

Michael Walther

Michael Walther

CEO - Akquinet Americas Corp.

“We are very proud to have gained one of the most competent partners in South Africa specializing in Business Intelligence with Modernising Management. We see South Africa as a very interesting growth market with high potential, especially regarding the native e-strategy for the digital transformation of economy and society. In our opinion Modernising Management has the optimal product mix of data management, data visualization and predictive solutions to provide its customers with the best possible support on their way into the digital future and thus make better data-driven decisions and gain a significant market advantage.”

John Paul Kirton

John Paul Kirton

MD - Modernising Management (Pty) Ltd

“This partnership is very strategic for MM as we are a specialist financial services company that works within environments where Microsoft Excel is prevalent and does bring its challenges with regards to standardizing the data processes and standards outside an enterprise business intelligence product. This brings those standards together, so you can leverage the governance, security etc. in Qlik Sense with an Excel interface, visualizations and syntax for combining Qlik Sense data within Excel and using DAX on top for self-service requirements. I believe this opens up new opportunities for our Qlik customers. “

Michael Walther

Michael Walther

CEO - Akquinet Americas Corp.

“With our product AnalyticsGate 365 we want to make a contribution to this. This Excel add-in allows the full power of Qlik to be used in Excel. Users keep a single point of data when using their familiar Excel via the add-in. They can work in Excel based on the latest Qlik data. The security rules defined in Qlik still apply. This has convinced many controllers around the world. Additionally, AnalyticsGate can be used to implement automated reporting. An integrated alerting function will be presented soon. We are looking forward to a successful cooperation with Modernising Managament!”

Contact us for a demo of the AnalyticsGate Solution today!

Process Automation & Optimization With Data!

Process Automation & Optimization With Data!

Process Optimization

Process and data are core knowledge treads that run through and keep your organization competitive. They are the tendons of your organizational muscles. When you combine these two pivotal aspects of your competitiveness you reach breakthrough results in terms of strategic and operational execution of your competitive advantages.

Business Intelligence (BI) has become the active intelligence platform that automates your processes and produce actionable insights that drive improvements throughout your business.

This should not be done within a vacuum or silo.

When you improve your business it is essential that you take a cross functional or cross process handover look at what you are doing as you will only be moving the bottlenecks further downstream and when you automate poor processes you make them more-poor (poorer) quicker!

Active intelligence ensures that synthesis is created within your data to address information and insight requirements from your data. Should data thus not become the central point of your process performance programs? When your data is synthesized and an alert is triggered based on data changes and lands in certain ranges that have been identified or calculated though some algorithm to trigger an action, or a risk control or instruction to kick off another process such as moving data to your CRM, creating a lead, and notifying another person of such a lead for instance could certainly improve your competitive advantage.

With the Artificial Intelligence and advanced analytics capabilities today, there are thousands of these use cases within a single organization. Data will drive automation of processes but not in a massive process re-engineering type old school engagement, it will be an agile unpack the process and apply Data–>Insights–>Decision–>Action processes that are automated that will trigger the next best action based on the data and the insights.

Are we there yet?

Yes, we certainly are!

We have done many of these types of engagements but we are seeing the processes element becoming a major value add to such engagements as there are always change involved and when processes are changed and automated people are required to follow suit and change their roles and responsibilities accordingly or they will be left in a vacuum.

Short Case Study:

We did our first process optimization case 6 years ago for one global petroleum company.

We had a two-week period to identity why they “felt” they were spending so much on ad-hoc maintenance when they have a very good preventative maintenance program, and the annual costs were increasing substantially. We took the process approach to the project as it was clear we needed to understand the processes and procedures first, in order to understand the data. We were required to both unpack all the processes, roles & responsibilities, controls, and risks as well as all the processing data from their systems. We also performed some benchmarking processing tests for system processes.

The key findings and recommendations were provided according to the systems, people, and processes criteria. We recommended small process changes for the team at the end of the day which took very little training, but small things lead to large improvements from an optimization, control, risk management and cost savings point of view. The great thing about planning based on data is you can apply various scenario planning and predictive models to understand how a certain change to the business will affect your upstream, mid-stream and downstream processes.

We had a scope change as they wished to see more analysis around their asset management, but the end result was that in three weeks of work we were able to save them R 40 Million annually.

Next Steps

Take the agile process optimization approach today and start incrementally improving your business from the high impact priority areas first. We link all our process performance and optimization work to strategy to ensure your strategic objectives are in line with your execution of your business.

Do you want to see this case study? Download our brochure now!

Would you like to talk it through and see whether you would like to take this approach to your own analytics? Please contact us

Read More:

Sales Recommendations

Sales Recommendations

Sales Opportunity Identification

Context

The key to customer retention is customer satisfaction! The seller/buyer relationship is improved through insights into the customer purchasing behaviour and the more you dazzle your customers, the better you get to retain them. Sales drives a business, and producing more drives growth.

This article presents a sales opportunity identification model in the form of a recommender system for sales at customer level, over some accounting data.

Introduction

A customer with some capital, and in need of goods, seeks to find a supplier for the goods. A supplier/seller of the goods also seeks to find a customer seeking the goods. When the two meet and reach a money-vs-goods (or goods-vs-goods) exchange agreement, a sale result.

If the customer is happy with the goods supplied and the supplier is happy with the trade, an opportunity of another trade exists, given that the supplier has enough continuous supply, and the customer frequently needs the supplied goods. These are the kind of opportunity that we are interested in. We are interested in finding these opportunities, and activating them through a sales team.

Any sales expert would concur with the claim that “if they always come to buy, then we need only identify those that don’t always, and get them to always come to buy”. What this translates to is that, a well-established loyal supplier-customer relationship needs more maintenance than sales opportunity identification and advertisement. Only those relationships that aren’t steady need to be stabilised.

This idea, when projected to sales at item level per customer, opens a whole new perspective/dimension which allows for hidden but frequent opportunities, even to customers with established loyal relationships.

The methods used to build the model extends from statistical analysis (probability and set theory), and augmented with combinatorial methods for simplification and scientific computing (computer algebra) for computational efficiency over massive datasets. A high-level overview of these methods follows below.

Recommendations Model

Base Insights

Sales data, depending on the type of company, type of sale, and type of customers, follow the three (3) main v’s of big data analytics, volume, velocity, and veracity. The three v’s can help classify/categorise companies into different groups with respect to sales. These groups do not enjoy the same benefits/advantages with regards to recommendations across various algorithms. For instance, a supermarket such as Spar has too much variance in its customer space, which changes highly everyday as new customers visit the store, and old customers not showing up anymore. These kinds of companies can benefit better at headquarters level where the individual franchises are modelled as customers. For companies such as motor vehicle rentals (business-to-business), the client base is somewhat steady, and therefore may benefit from the model built. In essence, four variables contribute to a measure of benefit from the algorithm built, and they are:

 

  • Type of business
    • Stability in the client space
  • The 3 main v’s of big data analytics

Frequent Itemset Mining

Frequent Itemset Mining (FIM) is a thematic area in data science that is concerned with the mining of patterns in terms of sets/groups that appear together frequently. A variable such as time can be used as the independent dimension for grouping of items that appear together. Think of a shopping cart, and or a receipt. Each receipt points to a group of items that are bought together. If multiple receipts are analysed, some item pairs may appear frequent in multiple receipts. Future sale recommendations can be projected from these receipts. However, there are two (2) cases which diverge from each other:

  1. Identity of the customer pointed to by the receipt is not vital
  2. Identity of the customer pointed to by the receipt is vital

In the former, a business such as an online shopping site (Amazon, etc) project their recommendations with algorithms belonging to such a case. For example, collaborative filtering algorithms such as Singular Value Decomposition (SVD), with some assistance from customer ratings. In the latter, the recommendations are customized directly to a customer. AI impersonation algorithms are also applicable in this case.

For sales, and data for accounting customers, the invoiced customer information is known, and therefore drove the choice of the model(s)/solution developed for our customers.

The Process

A probability measure or likelihood is calculated and used to predict whether an item is more likely to be purchased based on historic vs recent customer purchases (sales). Each probability is validated against a measure of possibility of the item just being bought more frequently in which case the high likelihood is classified “not interesting”, otherwise ‘interesting”. All interesting associations are returned as recommendations or sales opportunities for a particular customer.

Strong measures are put in place to infer, dynamically, how far into the customer’s sales history we need to dig, and how far back into the history is recent enough to make good inferences. These measures are dynamic, that is, vary per customer.

Since some companies have data with high velocity, and thus high volume, processing multisets for such is computationally heavy. Computer algebra techniques, together with parallel computing were used to speed up this process, and with custom but dynamic thresholds, a massive performance boost was achieved, reducing the initial runtime by over a thousand percent.

Deployment

Similar to our cashflow predictions, our analytics work is done mainly in Python, however, we are able to build equally competent solutions in Java, Scala, R, C#, Node.js, and C++. Python was chosen because it allows for implementation of complex algorithms with minimal code length, coding time investments, and is relatively easy to maintain.

Our analysis are performed with Python 3, and results pushed into a storage cluster where various connectors are made to serve presentation layer engines such Qlik Sense or Power BI. This work is built into our prebuilt accounting analytics solution.

Data leadership begins with data literacy.

Data leadership begins with data literacy.

Data analytics technology is better than ever. In fact, today’s solutions have developed so quickly that most employees don’t have the skills to harness their power. Businesses are at a tipping point in becoming data-driven, and the workforce skills gap is a barrier.

The Human Impact of Data Literacy, a recent report from Accenture and Qlik, reveals the many opportunities a data-literate culture can deliver ― including advice for how to get there. And at the webinar The Human Impact of Data Literacy, you’ll learn more about the findings, including:

  • The size of the opportunity ― up to $500M**
  • The 3 main barriers to becoming data-driven
  • 5 steps for building a data-informed workforce
  • Practical advice from featured analyst Forrester plus industry leaders at Nemours and Qlik

(**) – The Data Literacy Index, commissioned by Qlik and conducted by IHS Markit, PSB Research, and academics from the Wharton School at UPenn.

 

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