Considering innovation as a system and having a goal to embed it within one’s organization is neither an easy task nor an impossible one. If this is the primary objective, and if this aligns with the consensus of all stakeholders, it becomes crucial, before commencing any actions, to adopt a mindset focused on innovation, akin to how one concentrates on developing the organizational direction to enhance revenue and profit.
This implies that to succeed, the same level of effort and methodology must be directed towards developing the organizational strategy and executing the most effective and efficient innovation methods. This involves clarifying the purpose, establishing the right mission (the reason behind the initiative and the desired impact), and defining values (principles guiding all stakeholders). Internal environmental analysis (identifying organizational strengths and weaknesses related to capabilities, resources, assets, skills, and competencies) and external environmental analysis (recognizing external opportunities and threats) are also crucial. Subsequent steps include performing SWOT analysis (aligning external opportunities and threats with internal strengths and weaknesses), conducting scenario planning (suggesting strategic scenarios based on SWOT analysis alignment to set necessary objectives), and identifying value drivers (features distinguishing the value generated from the innovation strategy).
Based on the aforementioned, it’s imperative to create a vision (the long-term goal for the innovation system), establish SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound) objectives, select appropriate and balanced key performance indicators (KPIs), develop sound and aligned initiatives (supporting the achievement of selected KPI targets and objectives), and consequently, disseminate the entire innovation strategy throughout the organization at all levels.
Consequently, all stakeholders must align themselves, identify their needs and expectations, and determine how to meet these through the innovation strategy. Subsequently, they should proceed with the execution process, understanding and acknowledging the clear alignment between the innovation strategy and the organizational strategy.
It is essential to view the innovation strategy as a core success domain for the organization, understanding that progress, improvement, and profit growth are interdependent with the innovation system. Moreover, it’s crucial to ensure the involvement of all stakeholders in this system, while embedding continuous improvement as the primary driver in maturing the system over time. Similar to excellence, innovation maturity is an ongoing journey that continually brings added value, which should be appreciated and built upon.
The fourth industrial revolution has commenced. Linking it with innovation, transformation, future forecasting, and future change is pertinent, as they are all directly driven by and enabled by data management. Nowadays, the primary infrastructure for any company worldwide transitions from physical premises and branches to the cloud, where data are structured, organized, and interconnected, drawn from various sources such as customer interactions, product and service utilization, service and product development phases, defect management, product degradation, input and output resources.
This transition highlights that numerous data sources have been in place, yet not all have been utilized, analyzed, and transformed into information and knowledge. The shift towards big data and the advancements in artificial intelligence and conditional monitoring have changed the landscape. Decisions are now based on data, not just analyzed to reflect the current state but also organized and correlated to predict the future, facilitating decisions that secure not only the present or short-term future but also the long-term future.
This evolution underscores the importance of starting with the development of the right architecture to link various data sources, leveraging their mutual support and integration for greater benefit. It involves embedding in this architecture the correlation of data from different sources to build new components in the system architecture, adding value to the overall system. Understanding this aspect emphasizes the need to benefit from all data sources and install more sensors in development processes, products, streets, houses, cars, and everywhere, moving towards a products-as-a-service paradigm and eventually achieving the end goal of a planet-as-a-service, where data from everywhere are fed, analyzed, and used to identify new information and knowledge for the benefit of all.
The case study “Apple’s Future: Apple Watch, Apple TV, and/or Apple Car?” narrates Apple’s journey focusing on three products: smartwatches, smart TVs, and smart cars. It highlights how Apple has targeted the market and addressed customer needs to increase global market share and profit while enhancing the brand image. While this approach appears commendable, it aligns with the traditional viewpoint that continuous profit growth sustains a business.
However, from an alternative perspective, Apple has consistently aimed to shift from the red-ocean to the blue-ocean strategy, moving away from competition. The increasing number of competitors, open-source software, and global innovations necessitates larger leaps. Apple’s success also stems from co-creating value with its customers, understanding their needs, and embracing innovation and change.
Another facet is that Apple’s current endeavors represent a short-term strategy aimed at long-term value generation and delivery. Data serves as the primary driver, with all products and services yielding valuable data. This contradicts the notion that customers don’t know what they want; rather, it underscores the importance of understanding customer pain points and co-creating value with them.
Apple’s products evolve based on collected data and usage behaviors, generating new value with each iteration. The incorporation of health data into products like the smartwatch and analyzing consumer behaviors allows Apple to add value beyond traditional usage scenarios. Ultimately, Apple’s strategy mirrors a child playing a PlayStation game, controlling and directing the world.
While this may seem daunting and scary, proper use of data-driven strategies can benefit everyone, provided they are employed ethically and responsibly and not end up as Mikhail Kalashnikov puts it: “The fact that people die because of an AK-47 is not because of the designer, but because of politics.”
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About the Author
Malek Ghazo is a seasoned Senior Management Consultant with over 14 years of experience in the realm of organizational excellence (EFQM, 4G, Malcolm Baldrige), performance management, strategy planning/execution, and sustainability/CSR management. Throughout his career, he has cultivated expertise in developing benchmarking studies on an international scale. His clientele primarily consists of both public and private sector entities, to whom he provides invaluable services in organizational excellence, strategy planning and agile execution, KPIs and performance management models development and deployment, as well as EFQM model adoption and implementation. Geographically, Mr. Ghazo has dedicated his efforts to Europe (with a focus on the UK) and the Middle East, particularly in KSA, UAE, Qatar, and Jordan. Currently, he is engaged in pursuing his PhD at the University of Pécs in Hungary, with a focus on exploring the correlation between circular economy and organizational excellence and sustainability, aiming towards global sustainability.
Editor’s Note: This article was originally published on March 26, 2024 and last updated on September 17, 2024.
You’ve probably heard tech buzzwords like data-driven decision making, advanced analytics, “artificial intelligence (AI), and so on. The similarity between those terms is that they all require data. There is a famous quote in the computer science field — “garbage in, garbage out” — and it is a wonderful example of how poor data leads to bad results, which leads to terrible insight and disastrous judgments. Now, what good is advanced technology if we can’t put it to use?
The problem is clear: organizations need to have a good data management system in place to ensure they have relevant and reliable data. Data management is defined by Oracle as “the process of collecting, storing, and utilizing data in a safe, efficient, and cost-effective manner.” If the scale of your organization is large, it is very reasonable to employ a holistic platform such as an enterprise resource planning (ERP) system.
On the other hand, if your organization is still in its mid to early stages, it is likely that you cannot afford to employ ERP yet. However, this does not mean that your organization does not need data management. Data management with limited resources is still possible as long as the essential notion of effective data management is implemented.
Here are the four fundamental tips to start data management:
Develop a clear data storage system – Data collection, storage, and retrieval are the fundamental components of a data storage system. You can start small by developing a simple data storage system. Use cloud-based file storage, for example, to begin centralizing your data. Organize the data by naming folders and files in a systematic manner; this will allow you to access your data more easily whenever you need it.
Protect data security and set access control – Data is one of the most valuable assets in any organization. Choose a safe, reliable, and trustworthy location (if physical) or service provider (if cloud-based). Make sure that only the individuals you approve have access to your data. This may be accomplished by adjusting file permissions and separating user access rights.
Schedule a routine data backup procedure – Although this procedure is essential, many businesses still fail to back up their data on a regular basis. By doing regular backups, you can protect your organization against unwanted circumstances such as disasters, outages, and so forth. Make sure that your backup location is independent of your primary data storage location. It could be a different service provider or location, as long as the new backup storage is also secure.
Understand your data and make it simple – First, you must identify what data your organization requires to meet its objectives. The specifications may then be derived from the objectives. For example, if you are aiming to develop an employee retention program, then you will need data on employee turnover to make your data more focused and organized. Remove any data that is irrelevant to the objectives of your organization, including redundant or duplicate data.
Data management has become a necessity in today’s data-driven era. No matter what size and type of your organization, you should start doing it now. Good data management is still achievable, even with limited resources. The tips presented are useful only as a starting point for your data management journey.
Learn more about data management by exploring our articles on data analytics.
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Editor’s Note: This post was originally published on December 9, 2021 and last updated on September 17, 2024.
Nowadays, data analytics in the ICT (Information and Communication Technologies) industry is not just a byproduct of operations but a cornerstone for strategic decision-making. While many discuss the theoretical potential of data, few address the critical gap between theory and practical application. Leveraging data effectively can drive performance, foster innovation, and enhance customer experience.
ICT companies generate vast amounts of data daily, including sales, revenue and other financial components, as well as supply chain, project delivery, product performance, customer usage, service quality, and customer experience data. The challenge lies not only in the volume of data but in capturing, analyzing, and interpreting it effectively. Robust data storage and management solutions are essential for efficiently managing all of this.
Driving Performance with Data Analytics in ICT
Data analytics in the ICT industry can mean improvements in operational efficiency. By monitoring the right metrics, ICT companies can identify areas of improvement, optimize resource allocation, and streamline processes. For example, analyzing procurement data can determine whether to develop certain capabilities internally or outsource them. This analysis can inform strategic decisions on partnerships, negotiations, and resource allocation, ensuring actions are grounded in real-world insights.
Data can drive innovation by revealing client needs and market trends. ICT companies can tailor their services to meet evolving demands by identifying market gaps and opportunities for new products or enhancements. Continuous analysis of trends and user feedback ensures offerings remain relevant and competitive, addressing unmet needs and keeping pace with technological advancements.
In the competitive ICT market, customer experience is a key differentiator. Data-driven insights allow companies to personalize interactions, anticipate issues, and provide timely solutions. For instance, by combining technology adoption metrics with customer feedback and usage patterns, companies can understand why products are underused and make necessary adjustments. This approach enhances customer satisfaction and loyalty by addressing real-time needs and preferences.
A real-world example of data-driven decision-making is demonstrated by a company closely known to the author, which aimed to improve its win ratio for government bids. For confidentiality reasons, the company’s name will not be disclosed. The company embarked on a data-driven initiative, utilizing historical bid data, competitor analysis, and publicly available data from the official government bidding platform, leveraging robotic process automation (RPA).
The data analysis revealed patterns in winning bids and identified the average variance between the company’s bid prices and the next closest bidders. This analysis informed strategic decisions to revamp bid pricing strategy, tailor proposals to government needs, and reduce the bid price gap with competitors. The company also implemented various initiatives related to cost restructuring and improving partnership terms.
As a result, the company saw a significant improvement in its win ratio, increased revenue from government contracts, and better alignment with market expectations. This data-driven approach established a reputation for quality bids, built stronger relationships with government clients, and supported the company’s growth and strategic goals.
Investing in data analytics capabilities proved highly beneficial, leading to better processing of government bids, more informed decision-making, and reduced manual work. This experience was pivotal in revamping the company’s approach to challenges and increasing data utilization in various decisions.
A significant challenge in implementing data-driven strategies is the lack of a defined data catalog and metadata. Establishing clear data practices is essential for building a reliable foundation. Proper data management ensures privacy and security while enabling effective decision-making. These practices provide a framework for overcoming barriers and applying data-driven strategies practically.
However, creating a data-driven culture requires more than just tools; it demands a mindset shift. This involves building trust in data through proper management practices, empowering teams with data interpretation skills, fostering collaboration, and aligning goals with data-driven initiatives. Starting with a small, credible data set and expanding organization-wide can effectively turn theoretical concepts into practical applications.
The ICT industry is experiencing significant changes. Embracing data-driven decision-making, investing in data architecture, and adopting clear data practices can unlock new potentials, enhance client experiences, and improve efficiency. Failing to do so may result in inefficiencies and a loss of competitive edge. As competition intensifies, leveraging data effectively turns challenges into opportunities.
Deepen your understanding of using data for better decision-making and other aspects of business with our insightful articles on data analytics.
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About the Author
This article is written by Yazeed Almomen, a Corporate Planning & Performance Manager at one of the leading ICT companies in Saudi Arabia. With over six years of dedicated experience in the corporate planning and performance field across both private and public sectors, he has led numerous performance transformation projects and is passionate about building sustainable planning and performance management practices. He has a keen interest in leveraging data-driven decision-making to enhance corporate performance and foster innovation.
Big data is a major asset for businesses that can access its insights. Making this happen, though, is a complicated job that needs the right tools. Enter data enrichment.
Understanding how it works and its impact on current industries is a great way to get to know what data enrichment can do for your organization. How it benefits the use of big data will become clearer, too.
What Is Data Enrichment?
Data enrichment is the process of identifying and adding information from different datasets, open or closed, to your primary data. Sources can be anything from a third-party database to online magazines or a social network’s records.
People and organizations use data enrichment to gather legitimate intel on specific things, like a customer, product, or list of competitors. And they can start with just their names or email addresses.
As a result, the original data becomes richer in information and more useful. You can find education trends, profitable news, evidence of fraud, or just a deeper understanding of users. This helps improve your conversion rate, customer relations, cybersecurity, and more.
The most popular method of making all this a reality is specialized software. Their algorithms vary in strengths and weaknesses, as SEON’s review of data enrichment tools shows. They can target human resources, underwriting, fraud, criminal investigations, and more. However, the goal is the same: to support the way we work and give us better insights.
Data Enrichment and Big Data: What Statistics Say
Data enrichment is a good answer to the problem of big data, which often sees masses of disorganized and sometimes inaccurate information that often needs cleaning, maintenance, and coordination.
Creating a data-driven culture within organizations
Despite the benefits of smart data management and major investments already in place, only 24% of firms have become data-driven, down from 37.8%. Also, only 29.2% of transformed businesses are reaching set outcomes.
What this shows is that, yes, big data is difficult to deal with but not impossible. It takes good planning and dedication to get it right.
There are several promising big data statistics on FinancesOnline. For starters, thanks to big data, businesses have seen their profits increase by 8-10%, while some brands using IoT saved $1 trillion by 2020.
Also, the four biggest benefits of data analytics are:
Faster innovation
Greater efficiency
More effective research and development
Better products and services
These achievements are taken further with data enrichment, which adds value to a company’s datasets, not just more information to help with decision-making.
How Does Data Enrichment Help Different Industries?
The positive impact of constructively managing data is clear in existing fields that thrive because of data enrichment and other techniques. Here are some examples.
Fraud Prevention
Data enrichment helps businesses avoid falling victim to fraudsters. It does this by gathering and presenting to fraud analysts plenty of information to identify genuine people and transactions.
For example, you can build a clear picture of a potential customer or partner based on information linked to their email address and phone number. Do they have any social media profiles? Are they registered on a paid or free domain? Have they been involved in data leaks in previous years? How old are those?
It’s then easier to make informed decisions because we know much more about how legitimate a user looks.
Banking services, from J.P. Morgan to PayPal, benefit from such intensive data analytics, as do brands in the fields of ecommerce, fintech, payments, online gaming, and more.
But so do online communities, where people create profiles and interact with others. For example, fake accounts are always a problem on LinkedIn, mainly countered through careful tracking of user activity. Data enrichment can help weed out suspicious users in such communities, keeping everyone else safe.
Marketing
Data enrichment in marketing tracks people’s activities and preferences through cookies, subscription forms, and other sources. To be exact, V12’s report on data-driven marketing reveals Adobe’s survey findings regarding what data is most valuable to marketers.
48% prefer CRM data
40% real-time data from analytics
38% analytics data from integrated channels
Companies collect this data and enrich it to create a more personalized experience for customers in terms of interactions, discounts, ads, etc. Additionally, brands can produce services and products tailored to people’s tastes.
HR
The more information your human resources department has, the better it’s able to recruit and deal with staff members. Data enrichment is a great way to build strong teams and keep them happy.
Starting from the hiring stage, data enrichment can use applicants’ primary data, available on their CVs, and grab additional details from other sources. Apart from filling in any blanks, you can flag suspicious applicants for further investigation or outright rejection.
As for team management, data enrichment can give you an idea of people’s performance, strengths, weaknesses, hobbies, and more. You can then help them improve or organize an event everyone will enjoy.
Summing Up
As we saw in these examples, data enrichment already contributes to the corporate world in different ways, both subtle and grand.
With the right knowledge and tools, we can tap into this wealth of information even further, allowing it to make a real difference in how we work and what we know, rather than simply amassing amorphous and vast amounts of data.
Learn more about data enrichment by exploring our articles on data analytics.
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About the Author
Gergo Varga has been fighting online fraud since 2009 at various companies – even co-founding his own anti-fraud startup. He’s the author of the Fraud Prevention Guide for Dummies – SEON Special edition. He currently works as the Senior Content Manager / Evangelist at SEON, using his industry knowledge to keep marketing sharp and communicating between the different departments to understand what’s happening on the frontlines of fraud detection. He lives in Budapest, Hungary, and is an avid reader of philosophy and history.
