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Data Analytics

Making the most of Big Data and Advanced Analytics in companies – a brief introduction

The fact that the topic of data, its quantity and importance as well as everything around it is increasing, I will spare in this post. This should be more than known by now ūüėä Nevertheless, I see every day in projects that the topic of data is still treated very shabbily in many companies – sometimes unknowingly.

Big Data Analytics replaces intuition in decision making

Big Data has long since replaced gut feeling and instinct. This makes it even more important that big data and advanced analytics are used correctly in companies and thus support data-driven decision-making. It should be briefly mentioned here that the term big data does not actually apply to most companies anymore and that it is not the quantity of data but its heterogeneity that is usually one of the greater challenges in data processing and preparation.

A brief digression: In contrast to business intelligence, advanced analytics falls into the area of data science and goes beyond data analytics with its methods. Advanced analytics is usually based on advanced programming and modeling, which in most cases requires very large amounts of data. At its core, it involves the use of various forms of artificial intelligence, such as in data mining or process mining. In comparison to business intelligence, advanced analytics is thus not only related to historical events and their evaluation, but lives through the prediction of future events supported by modeling.

For example, advanced analytics can support e-commerce to personalize the customer approach and improve the customer journey by using existing customer data for detailed segmentation.

Big data and advanced analytics are all about asking the right questions. These questions should not only be known to data scientists or experts – but every employee should also know where to find information relevant to them and be able to interpret it correctly. After all, the results of analyses are only as good as the questions that are asked. Simply collating data will not achieve the goal. The questions should also integrate individual business problems and goals to determine what the analyses are intended to achieve. These questions can, of course, evolve or change according to the results during the big data analysis. Data literacy, the ability of employees to work with, understand and properly interpret the data, also plays an important role here.

Starting Big Data and Advanced Analytics

It is also important that the right data sources are evaluated and brought together accordingly so that the data can be combined and analyzed in a targeted manner. These sources should be defined in advance together with subject matter experts. The quality of the data also plays a role here: cleansing is often necessary here to correct any errors that may have occurred over years of manual data maintenance and entry. Especially to be able to clearly recognize conspicuous correlations and patterns in the company data, a clean linkage of the correct data is crucial.

In addition, big data analytics should be meaningfully integrated into the company’s daily processes. This includes, among other things, the right dashboard design to use relevant key figures in a targeted manner for data-based decision-making in the company. Structure, consistency, relevance, uniformity, visual perception, and content conception are important cornerstones here, which significantly determine the clarity of the data and its use. Collecting big data alone does not lead to the goal. The purpose and the various use cases of the collected data should therefore be defined in advance. Additionally, the topic of data governance plays an important role in clarifying the responsibilities and roles for the data as well as its organization within the company.

Closing Thoughts

Data helps determine the right decisions in companies. They also enable proactive responses to risks and opportunities and thus also influence the company’s success. For this purpose, the analysis of big data should follow certain objectives and incorporate empirical data. Advanced analytics methods differ from business intelligence through a future-oriented perspective and the use of elaborate technologies, especially artificial intelligence. By drawing conclusions from historical data, predicting events and developments, recommendations for action can be derived and processes and performance in the company can be improved.

Are you interested in Big Data and Advanced Analytics and the possibilities for your company? Get in touch with us!

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Data Analytics, Write

Optimizing forecasting – how looking into the future helps to improve planning

Flexible planning and holistic controlling are the basis for being able to react quickly to market developments and identify impulses for corporate management and development. Nevertheless, the digitalization of planning is faltering in many companies and is instead based on manual, time-consuming processes. In some cases, companies need several weeks “just” to implement sales or cost planning. The contradiction that on the one hand digital processes are supposed to increase efficiency and at the same time many companies still plan in Excel shows a strong need for action. If companies implement digital planning (and controlling), this can quickly become a competitive advantage.

Such competitive planning includes, among other things, forecasting. With these forecasts, companies can create realistic predictions for their planning based on existing data and thus react quickly to new market situations. A BARC study from 2018 already shows that predictive planning and forecasting provide more accurate forecasts than human planning and that their importance for companies has been increasing for some time.

Digital, user-friendly tools ‚Äď core element of efficient planning and fast forecasts

Software solutions have long since ceased to be the tools of IT specialists alone but should be available to professionals from various departments to optimize their processes. For comprehensive planning such as financial, sales or HR planning, it is not enough to just record and juggle a few key figures. Detailed analyses that ensure long-term precise planning are necessary.

So-called integrated business planning (IBP), which focuses on integrating sales planning with overall planning, is crucial for the further development of modern planning concepts. However, such complex planning can hardly be implemented manually in companies. Working in different systems and searching for and keeping several (Excel) files together is time-consuming and cannot really be done manually without errors. Therefore, software solutions in which the data is centrally available and allows the user to reschedule in an uncomplicated and flexible way are essential today.

Forecasting is an important part of integrated business planning

In addition, forecasts enable companies to plan their budgets accurately within the planning process and to calculate them precisely. Then, by creating a budget at the beginning of the year and using forecasts to combine current actual metrics with budget figures during the current fiscal year, the respective department can quickly and accurately adjust and allocate the budget based on past data in the best possible way.

An advanced simulation-capable planning model thus makes it possible to obtain information about expected future deviations in planning at an early stage and based on this, to take the right measures to close possible gaps and adjust sales, cost, and investment budgets. In addition, forecasts are also always closely integrated into the reporting process and so, for example, with our extension Write!, target and actual comparisons can be reported and commented together within the company.

Closing Thoughts

Forecasting methods can be used to quickly identify past and future developments in the company based on data and to include them in planning. In this way, complex planning can be simplified, planning processes accelerated, the informative value of reports strengthened, and budgets made more concrete.

Are you still planning with Excel and not yet using forecasts? Find out here how you can save time and money with integrated business planning.

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Stethoskop auf einem Laptop
Data Analytics, Use Case, Use Case - EN

Process mining ensures process improvements in medical technology

Challenge

A German medical technology provider has very complex sales processes, as a number of stakeholders such as health insurance companies, patients, doctors, nursing services, etc. are involved in a single order, and completely different factors come into play depending on the situation. The processes are hardly monitored and controlled. Both internal and external factors (organizational/regulatory) make it almost impossible to define homogeneous processes.

Solution

Based on the data analytics tool Qlik, a process mining solution was developed that prepares the purchase-to-pay process of various business units based on different data sources (mainly SAP) and processes it as event data with the customer- and business unit-specific business logics.

Added value

  • Data-based decisions for process improvements

  • Easy identification of bottlenecks and potential for automation

  • Fast detection and averting of deviations in control processes

  • Self-service process analysis & improvement

  • 100% process overview

Highlights

  • P2P processes can now be independently checked and tracked.

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Fabrikarbeiterin mit einem Tablet
Data Analytics, Use Case, Use Case - EN

Automatic reporting and prioritization relieves supplier management of manual tasks

Challenge

An international manufacturer of medical equipment wants to automate its reporting to external suppliers with supplier ratings and an overview of the status and urgency of orders. In addition, projects and assemblies are to be prioritized so that the associated subassemblies and individual parts are also prioritized. This is to be done across departments from project management to procurement and production to external production.

Solution

For the cross-departmental solution, all relevant data from the different areas and their prioritizations were combined. In addition, a specially developed user interface has been created that provides overviews for the various departments.

Added value

  • Supplier management employees are enormously relieved of manual time-consuming work through automation

Highlights

  • Several 100 suppliers are organized automatically

  • Automatic prioritization of purchase orders or production orders or external productions

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zwei Geschäftsmänner bei einem Mer
Data Analytics, Use Case, Use Case - EN

Automotive manufacturer creates fast and accurate planning for inventory and capacity

Challenge

The planning and analysis of nationwide inventories and capacities of a leading German automotive manufacturer are very complex. Nevertheless, these processes are important for the profitability of the company. In addition, a planning of upcoming scrapping quantities in combination with a stock requirement planning is to be created.

Solution

A data analytics solution was implemented in combination with our planning extension Write!, including master data management and a release process for plans. This was also integrated into the analyses.

Added value

  • Master data management for warehouses

  • Data-based planning solution for warehouse capacity and inventory development

  • 10.1% cost savings

  • 62.3% faster planning

  • 29.6% more accurate planning

Highlights

  • Mapping of a multi-stage planning process with data-based decision-making tools

  • Integration of plans into the analytics solution

  • Merging of history, snapshot and planning in one environment

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Zahnräder einer Maschine in der Nahaufnahme
Data Analytics, Use Case, Use Case - EN

Machine builder automates internal and external project overview

Challenge

A machine manufacturer for drive systems needs transparency towards its customers regarding major projects.

Solution

By linking the project data to production and procurement data from SAP, the data could be clearly prepared.

Added value

  • Clear project overview that is created automatically and can be made available to the machine builder's customers

  • Prevention of penalties

  • Quick identification of critical parts

Highlights

  • SAP PS collective orders combined with SAP PP elements

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Geschäftsmann, der ein digitales Tablet in der Fertigungsstraße einer Fabrik benutzt
Data Analytics, Use Case, Use Case - EN

Machine builder accelerates Big Data loading processes and optimizes data analysis

Challenge

One of Germany's largest engineering companies and automotive suppliers requires an analysis of more than 180 petabytes of sensor data from autonomous driving vehicles (including ABS, radar, camera systems, vibration, GPS signals).

Solution

A Qlik Sense cluster system was used to efficiently analyze the large volumes of data, using a Cloudera system with HBase / Impala.

Added value

  • After using the new system, charging processes could be accelerated from 3h to 3min.

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Hand zeigt auf Reporting
Data Analytics, Use Case, Use Case - EN

100% PSD2 compliant fraud reporting for payment service providers

Challenge

A European payment service provider has over 1 billion records per year and wants to improve its existing fraud detection. Current PSD-2 rules require enhanced reporting for all industry participants in a high security environment.

Solution

Fraud reporting based on data from the RiskShield fraud detection software was developed. This enables the creation of ad-hoc reports for clients and their customers for PSD2.

Added value

  • Fraud reporting based on very large amounts of data

  • Customer became supplier for PSD-2 reports of its customers

  • PSD2 Fraud Reporting Conformance for client and its customers at 100%

Highlights

  • Application monitoring for business-critical applications

  • Analysis of product usage behavior, marketing activities and customer structure

  • Data-based answering of conceptual & strategic questions

  • Automated visualization of data from different source systems

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Medizintechnik
Data Analytics, Use Case, Use Case - EN

Global medical technology company gains central overview of all relevant key figures

Challenge

A globally operating company in the eye diagnostic sector needs a connection of different systems (PPS, BDE, customer service, quality management, personnel, production control) to merge the data from the different systems with the different databases or interfaces.

Solution

The data from the different systems was transformed, cleaned and merged.

Added value

  • Transparent overview of all company key figures

Highlights

  • Data sources Postgres SQL, MSSQL, REST API were connected and linked to form an overarching management dashboard

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Besprechung mit Laptop und Tablet
Data Analytics, Use Case, Use Case - EN

Increase of usability and creation of performant analyses in the area of online banking

Challenge

One of the world's largest banks needs an efficient data analytics platform integrated into its online banking portal to optimize the analysis of stock portfolios and currency rates.

Solution

The data analytics software Qlik Sense was implemented in the online banking portal. For a seamless integration of Qlik Sense, a mashup of Qlik Sense (HTML/CSS and APIs from Qlik) was created via web technology and completely adapted to the bank's corporate design.

Added value

  • Increased usability and service in the online banking offering for bank customers

  • Performant analyses of the own portfolio

Highlights

  • Integration of Qlik into a safety-critical, productive end-user relevant area

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