This blog focuses on strategies for transforming business data into real-time, AI-powered business intelligence, and outlines the requirements of directors, CEOs, and leadership teams to increase their skills in managing business literacy. data.
There have been many definitions of what big data is over the years, ranging from Gartner describing the analysis of big data as high volume, high speed and wide variety assets that aid in decision making. Other academic experts, like Ackoff, have focused on converting data into cognitive value or wisdom, where an organization focuses first on data, then information, then knowledge, then insights. meaningful ideas or wisdom.
According to Wiki Encyclopedia, big data is a field that deals with the means to analyze, systematically extract information or otherwise process datasets that are too large or complex to be processed by data processing application software. traditional.
According to Technavio, the big data market will see incremental growth of over US $ 247 billion at nearly 18% CAGR during 2021-2025. This incredible growth has been driven by the acceleration in the adoption of consumer applications, which has resulted in huge volumes of structured and unstructured data.
With the data tsunami and the increasing adoption of Industry 4.0 digital transformation agendas, analyzing big data for more wisdom (i.e. ideas) is increasingly a skill. essential for critical operational and managerial decision-making practices.
Technology architectures of moving all corporate data to the cloud so end users can easily access and distribute data access across multiple machines, while using pooled power and storage to overcome bottlenecks. Monolithic throttling is a top priority for CIOs globally.
I remember when Hadoop was released in 2006 by Apache, co-founded by Doug Cutting and Mike Cafarell as a project, originally based on Google’s Google File System white paper. Amazon is even based on Apache Hadoop, a Java-based software programming infrastructure that supports the processing of large data sets in a distributed computing environment.
Fast forward to today and there are hundreds of great data platforms to choose from including market leaders such as: Databricks Lakehouse Platform, GoodData, Google BigQuery and Google Cloud, Cloudera, Hortonworks Data Platform , IBM SPSS, Microsoft SQL Azure Server Platform, Pentaho, Snowflake, etc.
Top 5 Big Data Challenges Executives Should Take Seriously
1. Access to knowledgeable professionals – Big Data management is a complex business and requires skilled expertise, ranging from: data software engineers, data mining analysts, data visualization experts, data scientists, communication and management expertise change and, more importantly, business process experts who understand the process links and results metrics to operate the business. There are risks in all large multinationals when it comes to data management and in mid-level and emerging management companies the risks are even higher. Take a quick survey of your board, CEOs, and leadership teams, and ask yourself these two questions: Is anyone certified in data management? Then move on to your next level of leadership, vice presidents and directors and ask the same question. If you want to be more alarmed, dig deeper and ask how many members of the leadership team are trained in AI, machine learning, and advanced analytics and repeat that question at the second level. I have advocated more accelerated digital literacy training for all executives and certifications in order to modernize more effectively, and I have to admit that in all the organizations I talk to these days, the challenges of my zoom calls, to Data traceability and data accessibility and data management persist everywhere.
2. Guarantee actionable information – It’s easy to get lost in the world of data conflicts and create Power BI or Tableau visualizations etc. – but the real key is to make sure organizations have actionable information that business users value and have for continuous improvement. Increasing visualization design skills is essential to enable these tools to be carefully designed and also ensure compliance with various standards such as International Business Communication Standards (IBCS), which can help derive best practices. for business analysis reports, and more importantly, organizations need to ensure that they pay more attention to ADA and WCAG accessibility compliance standards.
3. Correctly lay the databases – many companies fail in their big data initiatives in their vision to migrate all customer and supplier data to the cloud, and then provide end users with the data search tools and the meaningful creation toolkits of visualization, only to find that there is a lack of knowledge and insufficient understanding of what data fields mean, items and how they generate formulas to predict metrics. Sufficient data dictionary, data lineage, and operational data ownership frameworks are essential for managing data with risk controls.
4. Mastering unstructured data and documents – The majority of a company’s knowledge today comes from unstructured data sources where most of the wisdom resides and where it is not easily accessible or exploited due to expertise, development of Insufficient policies and controls over the management of unstructured data sources. Note: There is an oft-repeated statistic that 90% of all data that exists today was created in the last two years. IDC already predicts that the total sum of global data will be 175 zettabytes by 2025, up from 33 zettabytes in 2018. To appreciate the magnitude of this point, one zettabyte equals roughly 1,000 exabytes. One exabyte has the capacity to hold over 36,000 years of HD quality video … or stream the entire Netflix catalog over 3,000 times. In addition, it is estimated that over 90% of an organization’s data is unstructured data, taken from your PowerPoint, Word, Excel documents, text files, social media, videos, etc., and grows by over 55 to 66% each year. It is difficult to track and manage this knowledge, and only the advanced research capabilities of AI will enable companies to meet the challenges of unstructured data. Top companies featured by Gartner Group recently include Coveo, Google, IBM and MindFreeze in Canada (what a cool name!). See more Gartner Group information on advanced AI search solutions here.
5. Data security. I recently spoke at an international cybersecurity conference to discuss the value of AI innovations, but also how advanced cybercriminals always stay ahead of the market by investing in their own strategies. digital transformation. With increasing knowledge through smart cities, IoT always on connected devices, mobiles (Bring your own device – BYOD to work trends) and cloud initiatives, then layering the technologies of AI and machine learning, we’ve created a perfect world for a perfect storm where these cybercriminal engineers can more easily distribute malware in highly targeted methods and reach a wider audience with invasive displacement probes. Cyber security is one of the main concerns of directors and CEOs and it needs to be taken very seriously and more and more as more and more employees are working from home which creates more risks and vulnerabilities.
Organizations need to plan for traceability to ensure that core business processes and operational functions support big data perspectives, both top-down and bottom-up, and be agile to meet various stakeholder requirements, while Educating stakeholders on data management risks and ensuring appropriate controls are in place for data sourcing and production risk assessment are areas where all businesses need to improve. It’s easy to use words like digital transformation, but remember that CIOs can’t go further in building strong technical infrastructures to manage data and enable access. Business leaders need to improve their leadership skills and knowledge in the area of data management, as well as in the areas of AI and machine learning. It is a business imperative to evolve.
A good book to read is The AI dilemma Help administrators and CEOs improve their operational knowledge on the importance of data management and AI applications. Additionally, the Forbes Channel blogs I wrote in 2021 provide learning and development knowledge to help leaders increase their digital literacy in the field of advanced analytics, powered by AI.
Ackoff, RL (1989). From data to wisdom. Applied Systems Analysis Journal, 3-9.