datacamel.pl

For English see below.

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Zastanawiacie się jak przeprowadzić automatyzację procesu wsparcia klienta w Sprzedaży? Poszukujecie inspiracji i zachęty do zmiany swoich procesów? Nie wiecie jak sięgnąć po informacje z do swoich działań w fazie powitania klienta na swojej platformie e-commerce?

Przeczytajcie co Nasz główny Architekt, Kamil Gościmiński, ma w tych tematach do przekazania:

https://datacamel.pl/automatyzacja-wsparcia-klienta/

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Are you wondering how to automate the process of Customer Service in Sales? Are you looking for ideas and encouragement to change your processes? You do not know how to reach for the data captured by and use them in Welcome Phase on your e-commerce platform?

Read what our CEO and Head of Architects, Kamil Gościmiński, has to say on it:

https://datacamel.pl/en/customer-service-automation/



datacamel.pl

Customer Service Automation in Sales - follow your leads Customer service automation gives you insights on your clients to start a great conversation backed by data and boost your conversion ratio.

Pozwól sobie zaprezentować nowoczesne podejście do Inteligentnej Platformy Danych. Zajmie to tylko kilka minut. Szczególny nacisk w artykule kładziony jest na znaczenie słowa Inteligentny, niosąc ze sobą przekaz, że Hurtownia Danych w organizacji ma wywoływać zmianę nie tylko w sferze technicznej. Drugoplanowy staje się wybór narzędzi i technologii.
Najcenniejsze co można uzyskać wprowadzając koncepcje Hurtowni Danych do organizacji to zmiana myślenia pracowników, wartości za jakimi podążają i najważniejszych procesów samej organizacji. W szczególności dzieje się to poprzez wymuszanie na organizacji aby nawet najniższy szczebel kadry zarządzającej, był gotowy i świadomy jak (a także dlaczego), wykorzystywać dane jakie dostarcza Inteligentna Platforma Danych.

Czytaj dalej: https://datacamel.pl/nowoczesna-hurtownia-danych/

Hurtownia danych - podejście techniczne czy funkcjonalne? - datacamel Wprowadzając koncepcje Hurtowni Danych lub Inteligentnej Platformy Danych do organizacji zmieniamy myślenie pracowników, wartości za jakimi podążają i rewidujemy najważniejsze procesy samej organizacji.

jest sposobem, nie celem! To samo tyczy się narzędzi

Budując hurtownię danych dla Naszych klientów nie rozmawiamy na początkowych etapach co i jak wykonamy oraz o technologii i podejściu do architektury.

Cała idea hurtowni szytej na miarę zaczyna się od rozmowy o wsparciu Sprzedaży, Logistyki, Produkcji, Marketingu oraz odblokowania potencjału analiz dla działu Finansów i Kontrolingu. Rozmowa i diagnoza tego z czym ma obecnie kłopot organizacja w obszarze danych jest podstawą do zaplanowania czym będzie hurtownia danych, co więcej na jakie trudności będzie odpowiedzią. W tym przypadku system ma jedynie wartość dodaną dla Twojej organizacji.

Dla Nas produkt z półki oferuje "funkcjonalności", produkt szyty na miarę oferuje odpowiedzi na bolączki Twojej organizacji.

Zapraszamy na warsztaty, na których przygotujemy zestaw rekomendacji i funkcjonalnego zaadresowania zdiagnozowanych wyzwań organizacji. Wtedy będzie moment na decyzje o budowie, rozbudowie czy zaniechaniu inwestycji w hurtownię danych.
Rozmowa o wybranej technologii, architekturze, sprawach technicznych nadejdzie, ale to jest jeden z końcowych etapów, do których wspólnie dojdziemy.

Konsultanci dostępni dla was:
Grzegorz Gruszka - [email protected] -tel. 796 329 642
oraz
Kamil Gościmiński - [email protected] - tel. 500 858 222

Przeprowadziliśmy nową analizę pokazującą dlaczego jest i będzie ważnym graczem na rynku usług .

https://datacamel.pl/microsoft-wiodacym-dostawca-rozwiazan-business-intelligence/

W artykule znajduje się:
1) Ocena Power BI przez Gartner - Magic Quadrant
2) Opis architektury Power BI w oparciu o chmurę Azure
3) Wpływ ekosystemu usług i narzędzi oferowanych przez Microsoft
4) Argumentację przemawiającą za rosnącą rolą Power BI


zapraszamy do lektury!

Zapraszamy do kontaktu osoby zainteresowane tematem hurtowni danych, procesom ETL, budowaniu narzędzi Business Intelligence.

Wiele artykułów oraz treści publikowane jest na naszym portalu firmowym:
https://www.linkedin.com/company/datacamel
oraz na blogu:
https://datacamel.pl/blog/

Umów się z nami już dziś na warsztaty zerowe, na których zaadresujemy tematy związane z budową inteligentnych platform danych oraz podpowiemy jak rozwiązać bolączki Twojej firmy w zarządzaniu danymi.

Napisz na [email protected]
Czekamy na Ciebie

Wybór infrastruktury dla Hurtowni Danych. Estymacja kosztów.
Chmura czy serwer on premise?

Jednym z ważniejszych tematów przy budowie hurtowni danych, lub jej migracji, jest odpowiedź na pytanie: jakie środowisko wybrać dla infrastruktury IT docelowej hurtowni danych. Dyskusja ta ma kilka aspektów. W kontekście administracji i procesu deweloperskiego mamy sporo tematów, które w tym poście pominę, gdyż istnieje masa bardzo dobrych opracowań nad argumentami za i przeciw wybraniu jednego z rozwiązań.
Niemniej, dla osób decyzyjnych o projekcie (Zarząd, dyr. Finansów), kwestią kluczową jest porównanie kosztowe obu wymienionych opcji. Wielu Project Managerów mających przeprowadzić projekt hurtowni danych ma z tym duży problem, gdyż na co dzień nie zajmują się sprzętem, a koszty pozostawiają administratorom firmowych sieci i sprzętu IT.
Jak zacząć taką wycenę?
Posłużę się tutaj przykładem opartym o kalkulatory firmy Premium Serwer i chmury AWS.
…
Przeczytaj dalszą treść tutaj na Naszej stronie:
https://datacamel.pl/estymacja-kosztow-infrastruktury/
…
datacamel.pl specjalizuje się w doborze infrastruktury w projektach IT. Rekomendacje zawsze mają na celu optymalizację pracy zespołów i kosztów Naszych klientów. Nie jesteśmy związani z żadnym z dostawców, wybór zawsze pozostawiamy Naszym klientom.

Wybór infrastruktury dla Hurtowni Danych. Estymacja kosztów. - DataCamel Blog pasjonatów rozwiązań z obszaru hurtowni danych Wybór infrastruktury dla Hurtowni Danych. Estymacja kosztów. Chmura czy serwer on premise? Jednym z ważniejszych tematów przy budowie hurtowni danych, lub jej migracji, jest odpowiedź na pytanie: jakie środowisko wybrać dla infrastruktury...

Zapraszamy na stronę:
www.datacamel.pl

.pl

DataCamel - Tworzymy i wspieramy Hurtownie Danych i Systemy BI Tworzymy i zarządzamy - hurtownie danych, systemy BI. Automatyzujemy procesy biznesowe. Wyciągamy ukryte wnioski z Twoich danych.

It's now easier to contact datacamel.pl.

datacamel.pl updated their website address.

Data Quality in Insurance Company

First of all, let me ask you a question. What is the most vulnerable part of Data Quality process in corporation?

Some answers may be around technical aspects, like: ‘It is difficult to evaluate data’. Certainly that is true. Mostly when volume require Big Data techniques and infrastructure. There is no doubt that this is big project, require highly trained professionals and modern infrastructure.

So, it is the correct answer to question that was asked? No. Please, give me two minutes for brief introduction to Data Quality Process, then the answer will be easier to accept.

There are multiple steps in this process. Most of them are pretty obvious when you start to think about Data Quality. Let me focus on two most important ones. First is awareness of data quality and fair discovery of consequences of poor quality of organization’s data. As my experience shows, people are aware of this consequences but on different levels. Data Stewards are focus on processing quotes and some data quality issues mostly, add precious time to process single case. Process leaders in back office hears about Data Quality when someone wrongly entered data made huge impact on insurance policy, so client or front office discovered it (before or after there is becoming financial impacts due to huge payment or missing coverage for other institutions) . As I mentioned here other institutions, there are regulations and government institutions that obligate Insurance Company for proper DQ management. Regulations are last option, but sometimes government needs to repair some problems in industry sectors. Therefore, Top Management sooner or later will be into DQ process within company.

Great, so where is the issue there? How to cope with multiple expectations and lack of big picture? How to prepare Data Quality Process Owner for this task, when DQ process requires to work from bottom to top, from Data Steward that type data into forms (or client directly is filling data but understanding of data definitions is different between company and client) to ETL processes that feed Data Warehouse for Analytical Applications? There is no simple answer for that.

Secondly, there is Data Remediation Process. I have been to many discussions where parties made simple solution to deal with any Data Quality issue with single statement. Let’s start evaluate data during entering process (client forms or operational tool currently used in company). Then, there is always similar list of problems: we have more than one system in company. This checks will kill system performance. We do not own internal code to make those changes. Cost of technical implementations will be huge (Actually, there is a cost of bad Data Quality in organization. No one knows it on those meetings), etc. I stop here.

Even when we have Data Monitoring and perfectly prepared Exception Report (that includes wrong and expected values, with clear link to spot where it should be corrected, and by whom), it is not easy task to correct data in source system.

Finally, I can reveal the answer. Organizations don’t know who is actual Data Owner, processes are so distributed, forms were filled by employees that are no longer here, or we cannot even tell what is the correct data, as no one will ask again client for policy that is closed.

If you start to think about Data Quality within your organization, let’s help you with our experience. This process will be fully covered by my team. We will join you in this discussion with Board of Directors, Top Management or even with Technicians that manages your business apps.

PS. There is no better dashboard for monitoring DQ KPI’s than DQ Heat Map. This is a single tool for presenting DQ insights. This is quick check of critical company data elements.

Follow the Lead! Customer care automation in Sales explained

The process

Customer care is a core process for every company. We may think otherwise, but in the end it is the customer that is vital for your business sustainability and growth. This is why it is crucial to be able to handle your customer relations with both potential and current clients properly. While most of the time companies focus on opening new sales opportunities you should never overlook clents which come to us organically from various sources that directly dependent on our sales activities. It’s the Marketing division and it’s actions that generate new leads. Your Sales Team just needs to seize the opportunity and react quickly in order to warm up the lead and start the conversation.

Action – reaction

Speaking of new leads acquired organically the company should have a process that handles it, but before that happens, it should be able to quickly identify the occurence of this fact. Let’s assume that a potential client is already browsing through your online shop, but you haven’t acquired any contact details yet. By marketing activities (promotions, discounts, valuable industry related content) your company should encourage clients-to-be to leave contact details by inducing to register on your platform. Just by itself gaining e-mail address opens up a possibility to start the conversation.

After gathering contact details from the other side of the monitor you should react – quickly and thoughtful. Mature companies that follow the “customer first” policy treat this process as key in their sales model. Since a potential client has made the effort to share information you should greet him properly. This process should be build to present personalized offers, show off customer care and provide guideness.

Imagine that for this goal we are creating a robot, which tasks are:

1. To gather information about new lead based on online customer journey
2. Automatically assign an Account Manager (AM) based on client’s location
3. Generating a task for AM with the purpose of making the first contact and researching his needs by analyzing collected data and asking the right questions (data-driven decisions)
4. Sending e-mail to the client informing him about assignment of his personal AM and dedicated pricing model based on industry and location of the client.

Possibilities of automation

That first impression you make is so significant it’s indescribable. This is the time to show that you truly care. Profits will follow that. Such a process can be expanded by automatically assigning starting discounts for first purchase and many more. It can turn out that the client after coming to your e-commerce platform needs professional help with product selection suited for a particular purpose. Whatever you do, you must remember that there’s a person on the other side that may prefer more direct form of contacts and in this case it could be an appointment with Sales Team Representative.

Some of those activities can be automated. This involves the information flow both inside and outside of an organisation so that manual work is limited to it’s indispensable neccesity. Imagine the process in which Sales Representatives manually refresh reports in CRM to find newly registered accounts. It is a huge waste of time. Moreover, sales people often “fight” to contact the most promising leads first leaving the rest, probably less profitable ones untouched. Algorithms in automation can handle such situations by introducing assignment queue which takes care of equal division of clients and minimizes competition between employees over obtaining “better leads”. You surely don’t want others to think of your company as unprofessional because two different sales persons “bashed” the door of the same client. We could also enrich the process by sending personalized welcoming messages enriched by the knowledge gathered from online customer journey. Finally, we could add a calendar of assigned AM to schedule a call or meeting. That will definitely make a good first impression on your possible client and can be a great start for further conversation.

Case study

We have already implemented such projects, so we can introduce you to an exemplary work flow of such process automation. Traffic data from e-commerce platform was collected using Google Analytics and further analyzed. Gathered data is then processed outside of transactional systems to relieve it from heavy data processing. We integrated our solution with CRM (Customer Relationship Management) to present information inside the core company system.

Eventually, an AM receives a notification and task to greet new client inside CRM. The integration of the outcome of our data engineering takes crucial part thanks to which an employee doesn’t have to switch between different applications and can focus on working within one. We strongly count on simplifying day-to-day work for your employees and we do it by striving from introducing changes to your current technology stack.

Data Warehouse in Medium e-Commerce Company

Running a company may be seen as repeatable process of making business decisions. Let’s consider medium size e-commerce company. Obviously, there are different departments and processes within. As we may expect, there is not single application to cover all of needs and expected usability for organization. In this article, I would like to cover how to connect data into single source, allowing conformed data elements that allows BI applications efficiently visualize organization data, and finally to make accurate decisions on top of it. That’s is the underlying story for setting up a mature Data Warehouse (DWH) in any company.

There are few reasons why companies start building DWH. When company is growing, it is starting to produce more and more data in new areas of interests. Main factor then is to separate daily operations from analytics. This will free core systems from performing large scale reporting. DWH then will run, right after business hours, main ETL process (Extract, Transform and Load) to fed up data into own storage. This will not affect performance of operational systems when employees are using it. Since data feeding process will end, data warehouse has full night to prepare data for analytics and reporting. This allows developers to slice and dice data with very deep dive into data. With proper storage of facts and dimensions, BI applications will be free to go with most complex reports without paying in processing time. Moreover, growing company also start to introduce another operational systems when new processes starts. Data warehouse should be ready to add another source into ETL process. Therefore, Architect role is to chose suitable model (Kimball vs Inmon, Star vs Snowflake, etc.) to allow balance between flexibility and scalability.

Although, DWH may be seen as back up for company operational data, I will not cover that in this article. Moreover, there is also problem when DWH encounter multiple data source and how to conform it into single point of truth. Let me skip those topics, I will cover them in another articles.

E-commerce company is focusing mostly on sales: retail (B2C) or B2B. Sales is core for e-commerce company, but there are multiple other business areas that add value, or even give competitive advantage.

Company could consider implementing holistic ERP system. In real world examples, ERP is mostly introduced when manufacturing process is part of the business. In other case when products are purchased from another organizations, ERP is replaced by CRM (as vendors in that case are similar to customers). So, CRM in our example is first data source system. Then, there is Logistic and Warehouse for products. We assume that some products are imported, and some of them is good to have in stock, as this will give us more flexibility and credibility as supplier for B2B. This process will be in another smaller system, tailor made for keeping low cost of rotation and stock. Another part is Accounting and Finance, where Accounting has own structures. This will be connected with CRM to generate invoices and send it to clients. Then, there is also Analytics, that build data warehouse for BI system, and deliver data to Finance, Marketing and Management (Top Managements and Operational Leaders as well). All parties are somewhere as clients or suppliers for Enterprise DWH.

Building DWH in our example company structure, will definitely encounter problem of joining data from different sources. We have three systems, but DWH should present dimensional model across all of them. This will start conformance process. This topic will be covered in another article. There is also data quality issue, that has been describe in here: Data Quality in Insurance Company. Although, DWH were described just as an idea, I will finish it here. You need to wait for more technical blog to come.

Hurtownie danych. BI.