Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines
AnalyticsCreator

Looking Ahead: The Future of Data Preparation for Generative AI

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Generative AI is a significant part of the technology landscape. The effectiveness of generative AI is linked to the data it uses. Similar to how a chef needs fresh ingredients to prepare a meal, generative AI needs well-prepared, clean data to produce outputs. Businesses need to understand the trends in data preparation to adapt and succeed.

The Principle of “Garbage In, Garbage Out”

The principle of “garbage in, garbage out” (GIGO) remains as relevant as ever.  If you input poor-quality data into an AI system, the results will be poor. This principle highlights the need for careful data preparation, ensuring that the input data is accurate, consistent, and relevant.

Emerging Trends in Data Preparation

  1. Automated Data Cleaning

Manual data cleaning is both time-consuming and error-prone. Emerging tools now leverage AI to automate this process, identifying and correcting errors more efficiently. This shift not only saves time but also ensures a higher standard of data quality. Tools like BiG EVAL are leading data quality field for all technical systems in which data is transported and transformed. BiG EVAL utilizes plausibility and validation mechanisms to adopt proactive quality assurance and enable short release cycles in agile projects as well.

  1. Real-Time Data Processing

 Businesses are adopting technologies that can process and analyze data instantly due to the need for real-time insights. Real-time data preparation tools allow companies to react quickly to new information, maintaining a competitive edge in fast-paced industries.

  1. Improved Data Integration

Data often comes from various sources, and integrating this data smoothly is essential. Advanced data integration tools now facilitate the  merging of different data sets, creating a cohesive and comprehensive dataset for analysis. Managing a vast array of data sources is almost incomprehensible with data automation tools.

  1. Augmented Data Catalogs

Modern data catalogs are becoming more intuitive and intelligent. They not only help in organizing and finding data but also in understanding its lineage and context. This contextual awareness aids in better data preparation and utilization.

Adapting to These Changes

Businesses must be proactive in adopting these emerging trends. Here are a few strategies to consider:

  1. Invest in Advanced Data Tools

Investing in modern data preparation tools can  enhance data processing capabilities. Solutions like AnalyticsCreator provide robust platforms for real-time processing and seamless integration.

  1. Foster a Data-Driven Culture

Promote a culture where data quality is a shared responsibility. Encourage teams to prioritize data accuracy and consistency at every stage of data handling.

  1. Continuous Training and Development

The field of data science is constantly evolving. Ensure your team is up-to-date with the latest trends and technologies in data preparation through continuous learning and development programs.

  1. Leverage Expert Guidance

Sometimes, navigating the complex landscape of data preparation requires expert guidance. Partnering with specialists can provide valuable insights and help in implementing best practices tailored to your business needs. (Link to our partner page).

The Role of AnalyticsCreator

AnalyticsCreator helps businesses navigate the future of data preparation. By providing advanced tools and solutions, AnalyticsCreator ensures that your data is prepared, well-integrated, and ready for analysis. Its platform is designed to handle the complexities of modern data environments, offering features that align with the latest trends in data preparation.

In conclusion, as generative AI continues to influence industries, the need for high-quality data is important. By staying informed of emerging trends and leveraging tools like AnalyticsCreator, businesses can ensure they are prepared to harness the full potential of generative AI. Just as a chef’s masterpiece depends on the quality of the ingredients, your AI outcomes will depend on the data you prepare. Investing in your data can only lead to positive results.

Editorial Staff

process.science presents a new release

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Process Mining Tool provider process.science presents a new release

process.science, specialist in the development of process mining plugins for BI systems, presents its upgraded version of their product ps4pbi. Process.science has added the following improvements to their plug-in for Microsoft Power BI. Identcal upgrades will soon also be released for ps4qlk, the corresponding plug-in for Qlik Sense:

  • 3x faster performance: By improvement of the graph library the graph built got approx. 300% more performant. This is particularly noticeable in complex processes
  • Navigator window: For a better overview in complex graphs, an overview window has been added, in which the entire graph and the respective position of the viewed area within the overall process is displayed
  • Activities legend: This allows activities to be assigned to specific categories and highlighted in different colors, for example in which source system an activity was carried out
  • Activity drill-through: This makes it possible to take filters that have been set for selected activities into other dashboards
  • Value Color Scale: Activity values ​​can be color-coded and assigned to freely selectable groupings, which makes the overview easier at first sight
process.science Process Mining on Power BI

process.science Process Mining on Power BI

Process mining is a business data analysis technique. The software used for this extracts the data that is already available in the source systems and visualizes them in a process graph. The aim is to ensure continuous monitoring in real time in order to identify optimization measures for processes, to simulate them and to continuously evaluate them after implementation.

The process mining tools from process.science are integrated directly into Microsoft Power BI and Qlik Sense. A corresponding plug-in for Tableau is already in development. So it is not a complicated isolated solution requires a new set up in addition to existing systems. With process.science the existing know-how on the BI system already implemented and the existing infrastructure framework can be adapted.

The integration of process.science in the BI systems has no influence on day-to-day business and bears absolutely no risk of system failures, as process.science does not intervene in the the source system or any other program but extends the respective business intelligence tool by the process perspective including various functionalities.

Contact person for inquiries:

process.science GmbH & Co. KG
Gordon Arnemann
Tel .: + 49 (231) 5869 2868
Email: ga@process.science
https://de.process.science/

Editorial Staff

Role of Data Science in Education

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Data science is a new science that appeared thanks to a lot of reasons. The first reason is that nowadays, we have enough capacity to gather data and later work with it. The second reason is that society accumulates a lot of information every minute, and gadgets can save and then send it to data centers without any communication between people. But saving data is just one step in the world of science. The main task is how to analyze and show the results and later make a conclusion and prognoses. A team of online essay experts from a professional academic writing company SmartWritingService.com say that requests for academic papers for data science research topics increase extremely. It happens because data science analysts’ knowledge is useful in a lot of spheres, and the demand for such specialists is very high. Data science is an important part of sociology, political forecasting, the theory of games, statistics and others. Students need to study it and use it for future research. That’s why the universities added the courses of big data to be modern and meet requirements. Let’s try to discover the role of data science in education to make your own conclusion about its importance.

Why is data science necessary and how to become good in it?

The improvement of the studying process.

All students are different. They use different skills for studying and perceive information differently according to a lot of nuances. For some of them, the best way of getting information from lectors is listening without interruption. Other students prefer discussion during lectures. Some prefer to make notes. Others like to listen carefully and make notes later using the audio version of the lecture. Every group is different but has the same goal, and this goal is to absorb as much information as possible. The best assistant for this goal is data science that will show the teacher the best ways of communicating with students.

Using big data for personal needs. 

Have you ever thought that every one of us is a data scientist? We all use data, analyze it, and act according to conclusions. For example, shopping. Every time you go to the grocery, you notice how many people are there and how long the line is. When you plan your next shopping, you make a prognosis according to that data — the time you need to spend in the market and make a decision if it is optimal to go right now or it is better to visit the shop later when it is almost empty. The same thing works when we talk about studying. According to your observation and experience (that are both data), you make a conclusion on how much time you need to spend on every task.

Learning data science as an additional course. 

To know data science as an additional profession nowadays is very helpful. For the employer, it will be a bonus that can be a decisive factor. The skill to analyze is essential for every profession and helps to understand the market now and necessary for sales. The hardest thing for a data scientist is to ask the right questions for collecting data, and if you are good at it, your salary will increase immensely.

How to become a data science specialist?

Big data and artificial intelligence (AI). The importance of development. We believe that AI is the future of studying. First of all, the machine can collect the information and repeat it as much as the student needs. It is studying with the group, and the quality of communication grows rapidly. The base for machine learning in AI is data science. The analysis of data gives the set of possible reactions and actions to AI that can be changed or improved according to new data that was processed by AI. But from the beginning, AI is a huge set of data. It consists of reactions to life situations, speed and timbre of the voice of the interlocutor, country, the hour of the day, and a lot of other data that finally lead to the reaction of AI to the situation. The development of AI is a question of time, and it will help us to move faster in all spheres of life.

Can we ignore data analytics and don’t take a part in it?

The only way to ignore data science is to throw away all gadgets and become citizens of the wood cabin. And even this step won’t help. Those interested in the amount of population of people who live in the woods will be happy to add you to their list and analyze your +1 using data science technology. Every time you buy bread or don’t buy something on the internet, you are counted. Later they will analyze why you became or not their customer and save for statistics you age, sex, country of request, and all other parameters they can catch. We can only accept this reality and try to use it to our needs.

Don’t be afraid to become a subject for data science. It doesn’t affect your privacy a lot because there are billions of us, and we are only one point for statistics. Thanks to such analytics, scientists can make better specific offers and content for you. On the one hand, it is a kind of manipulation, but it saves time and resources for research from another hand. Use it to make your life more comfortable but remember that there are no coincidences when we talk about data.

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Signavio

What Customers Want from Business Transformation Solutions

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No matter what industry you work in, customer satisfaction is paramount. After all, in most cases,  there can be no business without customers! As such, keeping customers happy is a driving force for many organizations. All sorts of studies have been conducted, articles and reports have been drafted and re-drafted, and consumers bombarded with market research questions, all in search of understanding what customers want, and how businesses can offer it to them. 

Read this article in German:

Was Kunden von Business-Transformation-Lösungen erwarten

 

From a process management perspective, the technology to track the way customers interact with your business already exists, in the form of customer journey mapping (CJM). Customer journey mapping helps you to understand exactly how customers engage with your business, and what their experience is like when they do. It helps answer questions like: 

  • Do customers have positive or negative feelings when they interact with specific touchpoints within your organization’s processes? 
  • Are there points where customers stall, or disengage, or want more information? 
  • How do the people you’re trying to reach really respond to your customer service options?

Asking these questions internally is essential. However, an even more vital tool in building customer satisfaction and loyalty is a very simple one: just ask! 

What business transformation customers want

Technology enables businesses to ask customers directly about products and services more easily than ever, but there is an associated risk of consulting customers too much. Rather than letting customers know you care, this can instead make them feel the opposite. In addition, restrictions on the collection and use of customer data means actually contacting customers can be a challenge.

One way to overcome this is to make use of one of the technical review services available online. Browsing any of these sites reveals a wealth of information about what customers value in all sorts of industries. For example, Signavio uses IT Central Station to track customer views on business transformation software. When we consider these views in aggregate, we can see two common threads emerge over and over: collaboration and ease of use.

Comments from real users return to these points often:

  • “For me, the features I find most valuable are definitely in the Collaboration Hub. We are getting more users on there and becoming more familiar with it.”
  • “Based on my experience, one of the best features offered by Signavio is its simple Collaboration Hub functions, where users from various departments can constantly refer to their TO-BE process design.”
  • “One of the important things for us, when we were looking at solutions, was the ease of use. The ease of use affected the adoption in our organization massively. If it had not been easy to use and people were struggling with it, then they just would not have used it. So I’d say it’s quite a high factor in making a choice.”
  • “One of the most valuable features is ease of use, which has really been a good thing to put into the business. People like tools that they can just pick up and use straight-away.”
  • “The interface is quite intuitive. I am modeling a lot of processes, so for me, it’s quite easy.”

A final piece of advice

Knowing where customers find value is crucial to understanding how to meet their needs best, and thus creating ongoing and meaningful customer relationships. As with many customer-focused issues, feelings play a large role. 

The same can be said for business transformation, as the Lead Business Analyst at a media company with over 10,000 employees pointed out: “You will have a gut feel of what you want to do and when you actually look at the tools that are out there it is easier to make your decision.”

If you’re ready to make your decision about the right business transformation solution for you, register for a free 30-day trial with Signavio today.

Benjamin Aunkofer

Interview: Does Business Intelligence benefit from Cloud Data Warehousing?

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Interview with Ross Perez, Senior Director, Marketing EMEA at Snowflake

Read this article in German:
“Profitiert Business Intelligence vom Data Warehouse in der Cloud?”

Does Business Intelligence benefit from Cloud Data Warehousing?

Ross Perez is the Senior Director, Marketing EMEA at Snowflake. He leads the Snowflake marketing team in EMEA and is charged with starting the discussion about analytics, data, and cloud data warehousing across EMEA. Before Snowflake, Ross was a product marketer at Tableau Software where he founded the Iron Viz Championship, the world’s largest and longest running data visualization competition.

Data Science Blog: Ross, Business Intelligence (BI) is not really a new trend. In 2019/2020, making data available for the whole company should not be a big thing anymore. Would you agree?

BI is definitely an old trend, reporting has been around for 50 years. People are accustomed to seeing statistics and data for the company at large, and even their business units. However, using BI to deliver analytics to everyone in the organization and encouraging them to make decisions based on data for their specific area is relatively new. In a lot of the companies Snowflake works with, there is a huge new group of people who have recently received access to self-service BI and visualization tools like Tableau, Looker and Sigma, and they are just starting to find answers to their questions.

Data Science Blog: Up until today, BI was just about delivering dashboards for reporting to the business. The data warehouse (DWH) was something like the backend. Today we have increased demand for data transparency. How should companies deal with this demand?

Because more people in more departments are wanting access to data more frequently, the demand on backend systems like the data warehouse is skyrocketing. In many cases, companies have data warehouses that weren’t built to cope with this concurrent demand and that means that the experience is slow. End users have to wait a long time for their reports. That is where Snowflake comes in: since we can use the power of the cloud to spin up resources on demand, we can serve any number of concurrent users. Snowflake can also house unlimited amounts of data, of both structured and semi-structured formats.

Data Science Blog: Would you say the DWH is the key driver for becoming a data-driven organization? What else should be considered here?

Absolutely. Without having all of your data in a single, highly elastic, and flexible data warehouse, it can be a huge challenge to actually deliver insight to people in the organization.

Data Science Blog: So much for the theory, now let’s talk about specific use cases. In general, it matters a lot whether you are storing and analyzing e.g. financial data or machine data. What do we have to consider for both purposes?

Financial data and machine data do look very different, and often come in different formats. For instance, financial data is often in a standard relational format. Data like this needs to be able to be easily queried with standard SQL, something that many Hadoop and noSQL tools were unable to provide. Luckily, Snowflake is an ansi-standard SQL data warehouse so it can be used with this type of data quite seamlessly.

On the other hand, machine data is often semi-structured or even completely unstructured. This type of data is becoming significantly more common with the rise of IoT, but traditional data warehouses were very bad at dealing with it since they were optimized for relational data. Semi-structured data like JSON, Avro, XML, Orc and Parquet can be loaded into Snowflake for analysis quite seamlessly in its native format. This is important, because you don’t want to have to flatten the data to get any use from it.

Both types of data are important, and Snowflake is really the first data warehouse that can work with them both seamlessly.

Data Science Blog: Back to the common business use case: Creating sales or purchase reports for the business managers, based on data from ERP-systems such as Microsoft or SAP. Which architecture for the DWH could be the right one? How many and which database layers do you see as necessary?

The type of report largely does not matter, because in all cases you want a data warehouse that can support all of your data and serve all of your users. Ideally, you also want to be able to turn it off and on depending on demand. That means that you need a cloud-based architecture… and specifically Snowflake’s innovative architecture that separates storage and compute, making it possible to pay for exactly what you use.

Data Science Blog: Where would you implement the main part of the business logic for the report? In the DWH or in the reporting tool? Does it matter which reporting tool we choose?

The great thing is that you can choose either. Snowflake, as an ansi-Standard SQL data warehouse, can support a high degree of data modeling and business logic. But you can also utilize partners like Looker and Sigma who specialize in data modeling for BI. We think it’s best that the customer chooses what is right for them.

Data Science Blog: Snowflake enables organizations to store and manage their data in the cloud. Does it mean companies lose control over their storage and data management?

Customers have complete control over their data, and in fact Snowflake cannot see, alter or change any aspect of their data. The benefit of a cloud solution is that customers don’t have to manage the infrastructure or the tuning – they decide how they want to store and analyze their data and Snowflake takes care of the rest.

Data Science Blog: How big is the effort for smaller and medium sized companies to set up a DWH in the cloud? Does this have to be an expensive long-term project in every case?

The nice thing about Snowflake is that you can get started with a free trial in a few minutes. Now, moving from a traditional data warehouse to Snowflake can take some time, depending on the legacy technology that you are using. But Snowflake itself is quite easy to set up and very much compatible with historical tools making it relatively easy to move over.

Editorial Staff

New Sponsor: Snowflake

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Dear readers,

we have good news again: Now we welcome snowflake as our new Data Science Blog Sponsor! So we are booked out for the moment regarding sponsoring. Snowflake provides data warehousing for the cloud and has an unique data, access and feature model, the snowflake. Now we are looking forward to editorial contributions by snowflake.

Snowflake is the only data warehouse built for the cloud. Snowflake delivers the performance, concurrency and simplicity needed to store and analyze all data available to an organization in one location. Snowflake’s technology combines the power of data warehousing, the flexibility of big data platforms, the elasticity of the cloud, and live data sharing at a fraction of the cost of traditional solutions. Snowflake: Your data, no limits. Find out more at snowflake.net.

Furthermore, snowflake will also sponsor our Data Leader Days 2018 in November in Berlin!

Editorial Staff

New Sponsor: Cloudera

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Dear readers,

we have good news: We welcome Cloudera as our new Data Science Blog Sponsor! Cloudera is one of the most famous platform and solution provider for big data analytics and machine learning. This also means editorial contributions by Cloudera for at least one year.

At Cloudera, we believe that data can make what is impossible today, possible tomorrow. We empower people to transform complex data into clear and actionable insights. We deliver the modern platform for machine learning and analytics optimized for the cloud. The world’s largest enterprises trust Cloudera to help solve their most challenging business problems.

Learn more about our new sponsor at cloudera.com.

Editorial Staff

New Sponsor: lexoro.ai

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We wish our readers a happy new year and have good news: We welcome lexoro as our new Data Science Blog Sponsor for 2018!

lexoro GmbH is a Talent Management and Consulting company in the cosmos of the broad topic of Artificial Intelligence. Our focus lies on the relevant technologies and trends in the fields of data science, machine learning and big data. We identify and connect the best talents and experts behind the buzzwords, and help technology-focused industrial and consulting firms in finding the right people with the right skills to build and grow their analytics teams. In addition, we advise companies in identifying their individual challenges, hurdles and opportunities that go along with the great hype of Artificial Intelligence. We develop A.I. Prototypes and make the market transparent with industry-typical use cases.

Do you want to know more about lexoro? Visit them on lexoro.ai!