Tag Archive for: Big Data

Training: Ontdek de kracht van BI voor Financials

| 3-6-2019 | Alex van Groningen |

Wilt u een actievere rol gaan spelen in Business Intelligence (BI) en Analytics-projecten? Wordt u betrokken bij informatievoorziening ter ondersteuning van strategische en tactische besluitvormingsprocessen? Wilt u beter voorbereid in gesprek gaan met BI-specialisten? Dan is de introductietraining BI voor Financials voor u onmisbaar.

Ontdek de kracht van BI

BI staat al jaren bovenaan in top 10 lijstjes van zowel management als IT. Vooral door de recente mogelijkheden die big data analyse biedt om meer waarde te creëren. Ook in de financiële wereld is BI hot en wordt er, naast klassieke reporting lines, nadrukkelijk gekeken naar wat analyses kunnen brengen. Na deze eendaagse introductietraining weet u het ook.

Meld u nu aan

Volg de training BI voor Financials en ontdek wat BI is en wat het voor uw financiële organisatie kan betekenen. Verkrijg een 360° inzicht en ga direct aan de slag.

Periode: 1 dag
Investering: 995,-
Certificaat: 7 PE uren klassikaal
Datum: 6 juni 2019
Locatie: Hotel Fletcher Utrecht/Nieuwegein

Aanmelden

Uw voordelen

  • Begrijp de power van BI; haal alles uit de informatie die u in huis heeft
  • Word een volwaardige gesprekspartner voor BI- en IT-specialisten
  • Implementeer BI effectief, efficiënt en overtuigend in uw planning-en-controlcycli
  • Overtuig uw directie op basis van feiten en een onderbouwde BI businesscase
  • Lever financiële rapportages en analyses die zichtbaar toegevoegde waarde hebben

Onderwerpen

  • Wat is Business Intelligence?
  • Toepassingen voor BI binnen finance
  • Inrichting van en technieken voor BI
  • Gastspreker Jeroen Frenken; Manager BI Competency Center Schiphol Group
  • Organiseren van BI
  • Een businesscase voor BI

Boeken en materialen

Alle deelnemers ontvangen een map met alle presentaties die uitstekend als naslagwerk kan worden gebruikt.

Voor wie?

Bent u ook een financieel manager aan wie steeds hogere eisen worden gesteld? Wordt van u verwacht dat u mee kunt denken over IT vraagstukken? Wordt u ook geconfronteerd met complexe IT beslissingen en een steeds grotere aansprakelijkheid? Dan is deze BI introductie training voor u onmisbaar.

Incompany

We kunnen dit programma ook voor u op maat organiseren. Dat is al een goede optie bij vijf of meer medewerkers. Een financieel voordeel oplopend tot 50% van open deelname, een programma op maat en uitvoering waar en wanneer het u en uw organisatie uitkomt.

Vragen? Neem contact op!

Kan ik u van dienst zijn met een toelichting of advies?
Bel of mail gerust. Ik help u graag verder.

Ivo ten Hoorn, Product Manager Opleidingen
020 578 8911 / 06 2471 9757
[email protected]

 

Blockchain and big Data​: A great mariage

| 12-2-2019 | Carlo de Meijer | treasuryXL

Blockchain and Big Data are among the emerging technologies that are high on many companies’ agendas. Both are expected to radically transform the way businesses and organizations are run in the upcoming years. Long-time developing in a separate way, at first sight one might assume that these technologies are mutually exclusive. But that idea is rapidly changing.

There are growing expectations that distributed ledgers will help enterprises finally get to grips with Big Data, which thus far is struggling with a number of challenges. They are both powerful on their own, however when combined they may bring a large number of opportunities. Some even say that blockchain and Big Data are made for one another.

“Big Data is an incredibly profitable business, with revenues expected to grow to $203 billion by 2020. The data within the blockchain is predicted to be worth trillions of dollars as it continues to make its way into banking, micropayments, remittances, and other financial services. In fact, the blockchain ledger could be worth up to 20% of the total big data market by 2030, producing up to $100 billion in annual revenue.” Chris Neimeth, COO of NYC Data Science Academy.

In this blog I will look at what the interception of these two innovations may bring. Could blockchain be the solution for the existing Big Data issues and challenges?

Big data and data science/analytics: present challenges

Big Data is one of the fastest growing sectors in the world. Every business wants to get insights into usage patterns of their consumers. Massive datasets are thereby analysed using advanced statistical models and data mining. These Big Data sets will become even more prevalent over the coming years.

It’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analysed for insights that lead to better decisions and strategic business moves.” Data Analytics Company SAS

“Data analytics has become the key to corporate competitive advantage because of its role in identifying emerging market trends. In turn, companies can use this information to make quicker and better decisions that help them drive profitability”.EY

The rise of Big Data has presented a slew of issues for both big businesses and everyday consumers. With the growth in data good analytics is becoming all the more problematic. Some major problems to data management and analytics include so-called dirty data, inaccessible data, and privacy issues. And as Big Data increases in size and the web of connected devices explodes, it exposes more of companies data to potential security breaches..

With the advent of Big Data, data quality management is both more important and more challenging than ever. Companies that are dealing with large datasets should ensure that the data are clean, secure and not been modified and come from an authentic source. They have to make sure that the latest version is synchronized among all of the data centres in real time. It should also be ensured that these data are accessible. For most, however, the data silos are still a major issue and a full company-wide digital transformation is still more concept that reality.

Blockchain and Big Data: two sides of the same coin

Main question is: how do both technologies relate to each other, if any? Notwithstanding blockchain has not been explored extensively in aspects of Big Data management and analytics, both technologies could and should be seen as two sides of the same coin.

While blockchain is focused on recording validating data (data integrity), data science analyses data for actionable insight, making predictions from large amounts of data (prediction). While blockchain is changing data management, the latter is transforming the nature of transactions. Or said in another way: “If Big Data is the quantity, blockchain is the quality”.

Read the full article of our expert Carlo de Meijer on LinkedIn

 

Carlo de Meijer

Economist and researcher

 

Cash forecasting: A data story

| 17-01-2018 | Cashforce |

Have you ever heard the dogma that people only use 10% of their brain capacity? Fortunately, this statement is a myth, but a similar (and more truthful) argument can be made for data usage. Using the example of an oil rig, a 2015 McKinsey & Company report states that an organisation typically uses less than 1% of the collected data to make decisions. While intuitively not all data will be useful to include in the decision-making process, it’s fair to say that there is a huge untapped potential.

From advanced retargeting in the marketing world to tailored music suggestions on Spotify, data has been in an uplift, opening doors in almost every field. Corporate finance & treasury is sitting pretty as well: amongst other areas, integrating relevant data into your forecasting model can facilitate substantial improvements in the quality of your cash flow predictions.

In this exuberant amount of data, it’s important to distinguish internal from external data.

Internal corporate data

Put simply, the bulk of data involved in cash projections will be found internally. Standard forecasting models, mostly build in spreadsheets, often make use of a small part of these data. Both account balances grabbed from banking portals and user generated input contribute to fulfil the daily, weekly or monthly cash forecast. User generated data may contain sales budget & forecast, average incoming & outgoing cash flows, projected dividends, CAPEX investments, etc. This information is necessary however typically lacks accuracy.

When making smart use of additional internal business data, most of these estimates can be derived from other internal data that may lead to a higher degree of forecast accuracy and a maintainable forecasting model. Such internal data sources are numerous and contain information on sales & purchase orders, quotations from your CRM system, production planning & all kind of recurring activities that carry relevant information on your future cash flows. Additionally, treasury data can automatically be included as well, enabling your treasury department to be multiple steps ahead instead of running behind daily facts.

To maximize the potential of your internal (big) data, algorithms and calculations need to be added to the forecasting model. By incorporating customer payment behaviour, seasonality patterns, correlations between different types of cash flows… your predictions can easily benefit from fine-tuning of these basic parameters. Re-evaluating those assumptions can by looking at meaningful patterns that are present in the data, can help to make a smarter and more tailored forecast. As an example, by carefully looking at past payment periods, future payments for each customer can be estimated with a high degree of precision.

 External data

Finally, integrating external data in your forecasting model will typically not affect cash the forecast in the short-term. It can however be relevant for long-term cash projections and fine-tuning. Market sentiment and macro economical indices will be most useful here, as well as all ticker information on treasury & commodity futures.

After capturing all this data, it’s key to consolidate everything from several (usually incompatible) operational systems. Note that not only the amount of data and diversity of data sources are important, but the accuracy of input and up-to-date information as well.

Consequentially, through extensive modelling and analysis, an effective and accurate cash flow forecast can be created. For this you would need software that can handle advanced big data analytics in order to convey pattern recognition and forecasting. The lion’s share of prevailing software doesn’t have the necessary integration possibilities and processing power to efficiently effectuate these kind of complex consolidation and analyses. Fortunately, some are built with this data requirements in mind and do have these capabilities. These make room for generating a significantly better cash forecast.

The world of business is going through rapid advancement in this age of technology, and the financial discipline is not spared in this phenomenon. While this data story unfolds, the time has come to put your “corporate brain-capacity” to use.  Will you let this wealth of data create an unseen amount of value?

If you want to find out more about Cashforce and their services and products please refer to their company profile on treasuryXL.