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Datonix.it data quality assurance

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Business Process Re-engineering (BPR) How to fight Maverick Rebel. A too frequently overlooked cause preventing process optimization. This is one of the causes why too big companies are having issues which must be addressed DATA QUALITY ASSURANCE - DQA a typically overlooked domain.

Veröffentlicht in: Business

Datonix.it data quality assurance

  1. 1. Catch the Rebel Maverick Reducing Corporate ROI Data Complexity THE EXPERT VIEWPOINT
  2. 2. The expert viewpoint Alexander Kopriwa Big Data is a strategic facility to outwit the competition. Both big and small data discovery helps to identify trends, inefficiencies and risk exposures early. This delivers a true corporate strategic competitive advantage, when used correctly. It is extremely important, especially for small and midsized businesses (SME/SMB), to scrutinize new and emerging technology offerings discovering competitive threats and new business models to ensure enterprise survival and sustainability. This requires digital and technological know-how across all enterprise domains (beyond the IT department) where subject matter experts and data owners of various business processes are critical to shaping the future of corporate success. All organizations seek disruptive, cheaper and more agile utilization of all assets, including information processing resources. For example, manufacturing customizations require end-to-end digital processes cutting time-to-market delays, as well as, gaining needed agility, flexibility and optimal productivity. Implementing new strategic business goals often imposes business process reengineering (BPR) projects, like implementing a new ERP system such as SAP’s S4HANA (featuring high speed in memory data bases). Other requirements like Industry 4.0 and Internet of Things (IoT) architectures may also be of the essence. How long time available to analyze and report? 23% Not enough time to extract meaningful C-Level Dashboard insights. More then 77% of the controller effort is spent for data acquisition and preparation 77% For these reasons, substantial gains can be realized by modernizing the tools, and the methodologies, for use in data preparation. 2 Organizations are awash with too many and too poorly managed data assets. A strong correlation exists between high quality data from ERP solutions and other expert applications providers to empower and optimize business process efficiency. However, too frequently, one serious strategic omission diminishes above investments ROI. Organizations have hit on a rebel maverick inhibiting successful BPR and reducing overall ROI. This Data Complexity oversight is known as: Data Quality Assurance! How to Implement Data Quality Assurance (DQA)? Data capture and business process systems can be improved (at high cost), yet over time, the DQA wilts. Pressured employees tend to duplicate data records, hampering analytical process reconciliation and future optimization opportunities. Sooner or later, most enterprise business systems will suffer from poor DQA, lack of archiving, poor Meta data, inconsistent and incompatible formats. Organizations subsequently must rely on humans “opinions” to guide processes, as “we can’t trust the data in the system” (C-Level comment from a 1.8B USD food and beverage group, cited as an Asian prime company by global investment advisory firm). Digital business processes transformation reaches far beyond manufacturing, it affects all economic sectors, it is integrated within products and services (smartphones, automobiles, banking, insurance etc.). This generates data from previously unknown sources and usages, all capable to improve mass customization and mass production, in response to volatile market demand and fast changing needs. Copyright © 2016 datonix SpA www.datonix.it
  3. 3. 4 The DQA solution uses Datonix data preparation platform, it is a modern data management governance framework designed to resolve most analytic challenges. It improves DQA, including key ERP definitions of suppliers, customers, SKU (stock-keeping unit), BOM (bill of materials) and MRP (material requirements planning) and more. Management can quickly identify, correct, unify and validate data to avoid unproductive complexity costs. In addition Datonix FDG (fractal data guardian) ensures solid fiduciary audit trails of information assets which cannot be falsified after validation sign-off. All periodic procedures can be fully automated to save time, monthly reporting tasks can be augment with insightful graphical reporting, all configured to customer preferences both for desktops, tablets and smart mobile devices. In addition users can fully benefit from powerful data journalism tools like Dynamic-PDF, Dynamic-WordPress, Dynamic PowerPoint and custom formats, all fully secured. Datonix granular and accessible fractal-data-point management from the FDG, data points at lightening speed, accelerating data-mining, reporting and classification tasks. Datonix uses three simple steps: · Scan and correct relevant data-points from accounts and off-book records · Review, adjust, consolidate all relevant resource usage consumption · Publicize and disseminate analytical findings to the target constituents The expert viewpoint Alexander Kopriwa Using Datonix Data Complexity Analysis to ensure DQA All organizations periodically should audit and improve their data veracity (accuracy) before engaging in data driven BPR and strategic business process transformation projects: Data is a key economic asset and must be handled with care. Here reference master data management (MDM) records are critical to the good functioning of complex ERP and all business systems. Humans struggle to discern data quality, therefore potentially (erroneous) suspect data points must be identified, properly readied (structured, aligned) and analyzed to identify potential DQA issues. This process requires data adjustment (cleaning). The result is a clean (unified) data set followed by an Analytic step (i.e. data mining) delivering reviewed data using advanced classification using AI machine learning logic by exploiting the “K 4.5” data mining algorithm to discover similar records. And to finalize, all of the above is integrated into a data storytelling scenario aiding C-Level executives to fully understand all hidden issues so data stewards can apply and correct all DQA inconsistencies. The above MDM unification process needs to be repeated regularly, as humans have a tendency to create new duplicate master data when they cannot find part-numbers, customers or suppliers. Only when data is fully unified and quality assured, then the analytics become meaningful for strategic decision making… and BPR efforts can deliver better ROI with less risk. Conclusion Organizations must tackle data complexity, as early as possible, because hyper decision making agility imposes 100% data accuracy. This is difficult to achieve with ERP and transactional data base systems fed by humans, it requires infrastructure like analytical appliances or Cloud processing. Due to increasing business velocity and rapid change, organizational survival imposes near automated and periodic data (accuracy) audits to avoid unknown Data Quality Assurance Risks. Datonix DQA Solution Copyright © 2016 datonix SpA www.datonix.it DATA SOURCE Active Dashboard Operativie KPI’s Reporting C-Level Integrative Reports TBD DISCLOSE FIX AND ADJUSTMENT DATA MINING SOURCE SCAN CLEANSED DATA REVIEWED DATA Figure MDM and Reference Data Analysis
  4. 4. Biography Alexander Kopriwa, provides advisory services for organizations seeking competitive and strategic business advantages. Mr. Kopriwa has lived and worked on five continents and is fluent in five languages. Passionate about introducing innovative technology to organizations eager to become market leaders, he is also vice president of international expansion for Ontonix. A former research director for Gartner Group (and META Group), he is a proficient handler of global accounts and excels at applying modern digital methodologies to help clients define disruptive business processes. Mr. Kopriwa has more than 30 years of experience helping organizations focus on what matters for expansion and economic survival. Trained in international economics, IT architectures (Sweden) and change-manage- ment consulting (Austria), his expansive know-how covers an array of enterprise constraints in cross-cultural and international environments, including Mainland China, the United States, Europe and Southeast Asia. 6 The expert viewpoint Alexander Kopriwa Copyright © 2016 datonix SpA www.datonix.it