BIG DATA STRATEGY AS A SUCCESS MODEL FOR ALL INDUSTRIES?

BIG DATA STRATEGY AS A SUCCESS MODEL FOR ALL INDUSTRIES?

Big data seems to be both a blessing and a curse. Opportunities for growth and the potential for destruction are blurred in the sea of ​​data into a sometimes uncontrollable mass. But shouldn’t the simplified access to relevant company data bring more security to modern business planning?

In 7-10 minutes of reading time, our article should provide an overview of the following topics:

  • How big data & digital disruption affect the corporate world.
  • Which industries are regarded as pioneers in the age of big data.
  • How to sensitize your company to the challenges of digital disruption and create competitive advantages.
  • How data analytics & strategy can be combined in combination with data culture.

Big data – More effective, smarter and faster through larger amounts of data?

Raw data becomes information. Information creates knowledge. Knowledge from data analysis creates value for companies. The goal: to be able to record, harmonize, structure and ultimately analyze large amounts of data (with high data quality) from many different sources. In the course of digitization, almost unlimited storage space, cloud computing as “infrastructure” and faster computing speeds offer the ideal breeding ground for profitable evaluations. Data has therefore become an important part of business capital. In particular, the systematic approach in the field of data science offers companies a wide range of analysis options. In this way, unknown patterns are searched for in large databases in order to open up new opportunities for doing business. In addition, a multidimensional perspective on one’s own business model should be made possible. Data therefore forms the basis for finding knowledge. These discoveries reach into the future of a company or entire industries. The systematically developed forecasts of modern software solutions, such as so-called “prescriptive analyses”, are only used by 15 percent of companies in Germany. This occupies one of Study commissioned by KPMG .

Is it possible to design innovative products, services or business processes for the future in the past? Are future challenges and opportunities foreseeable? This sounds like a promising solution. However, so far only a few companies in Germany have used the latest analysis tools for customer data, for example. Possible efficiency losses and a lack of customer orientation can be the result. As a result, many opportunities to use data in the company to advantage remain unused. For innovative product development and targeted marketing measures, however, customer data can be valuable for a high degree of customer centricity.

But what actually is big data? Check out this video from  Funk-e Studios  :

The following types of data seem to be of particular relevance for companies:

  • Company data – master data, transaction data or project management data
  • Customer data – CRM data, customer behavior or social media
  • Publicly available data – log data, sensor data or location data
  • ystemically generated data – market data, scientific publications or regulatory data

The focus is on the question – what happens to companies that do not take advantage of these opportunities?

Big-Data Disruption – Unstoppable Dynamics of Digitization

No industry has a secret recipe that leads to unrestricted success in times of digital disruption. According to Harvard Business Research , 72% of companies fear that they too could be affected by the effects of an increasingly digitized world in the future . Especially with regard to so-called born globals such as AirBnB , Uber or Netflix, which blur entire industrial boundaries on a large scale. Often with simple, dynamic and cost-effective solutions that quickly crowd out traditional competition. Through highly innovative software solutions and important investors, they managed to expand almost worldwide in a very short time. The basis of these successes is not just luck, but rather a valid analysis of relevant data. The intelligent use of the available information is a source that brings innovation and sustainable growth with it.

German companies seem to have some catching up to do. Only 48% of companies use descriptive analysis for business planning. According to Bitkom Research, however, more is needed to secure and expand sustainable competitive advantages. Predictive analytics (39%) or prescriptive analytics (15%) as tools of suitable analytics tools are still used too rarely for intelligent value creation in Germany.

Data analysis is still too often carried out as an ad-hoc analysis with simple IT tools such as Excel or Access. Increasingly advanced solutions should contribute to secure and sustainable corporate planning. The keyword here is digital intelligence . The smart use of data for your own benefit.

According to Bitkom Research, the following aspects are particularly important for companies in order to minimize the business risk:

Industries in comparison – who benefits from data science and who has catching up to do?

The federal government is trying to demonstrate a completely new understanding and openness in dealing with data. Especially with a view to the draft amendment to the e-government law, the so-called Open Data Law. It is becoming clear that  the Federal Government’s digital agenda  sees data as the raw material of the future. Big data and data science are gaining in importance in many branches of the economy, at the latest since CeBit 2016. Being the industry leader in digital transformation based on big data – a key issue in many top management areas. According to the Bitkom Research Report “ Creating value with dataThis does not seem to be on the table at all management levels, at least in Germany. The report commissioned by KPMG decodes a very differentiated picture of how data science is used in the industries surveyed. Let’s first look at the results of the development of a big data strategy and how relevant the decisions based on big data science really are for companies.

For the latter, the question of relevance & decision-making, IT & electronics, health and the banking sector perform worst. In combination with the lack of implementation of actually derived measures, you bring up the rear among the 12 sectors surveyed. They state that they make few business decisions based on data. Small and medium-sized companies in particular do not yet have the appropriate concepts for positive implementation and implementation. Mechanical and plant engineering, automotive and insurance are among the top ranks when it comes to the relevance and decision-making based on data analysis.

If you look specifically at the development of a big data strategy, the picture between the sectors changes again. Around a third (34 percent) of companies state that they have a big data strategy. However, there are differences between the sectors. 56 percent of media companies and 46 percent of insurance companies integrate a big data strategy. The banking industry, which had previously indicated even less decision-making based on data, is also among the top places in the Bitkom ranking.

It should also be emphasized that although the automotive industry is a pioneer in the relevance and implementation of data science, only 34 percent of companies are pursuing a big data strategy in a targeted manner, according to the Bitkom report. Just like telecommunications and IT & electronics, they end up at the bottom of the list when it comes to strategic orientation.

But be careful: In an increasingly digitized world, it is no longer enough to just keep an eye on the nearest competitor or an isolated industry. Rather, a sensitization for one’s own performance gaps must be created. In conjunction with an analysis of comparable strategic groups , conclusions can then be drawn about the next measures.

A complex and extensive undertaking? It is precisely at this point that advanced analysis methods are used. These create fast response times to changing market conditions. But how can this succeed?

Data plan & data culture as tools for success

We will also provide you with a brief overview of the essential points that can lead to success in times of digital disruption.

Data analytics plan & strategy

Formulating a data strategy brings many benefits. KPMG conducted a global survey of 270 institutional investors, investment banks and analysts. The result: 62 percent of those surveyed are more inclined to invest in a company that has integrated data analysis into its overall strategy. Strategic and operational decisions can be implemented in a more targeted manner using the insights gained. The strategy pursued by the company becomes clearer, easier to plan, more controllable and, above all, more transparent. Pure human intuition in decision-making seems to be supposedly eliminated by analysis. So will emergent strategies soon be a thing of the past?

However, the long-term benefits of data analysis are countered by investments. A new data architecture, modern software solutions or data security can lead to disruptive changes in the entire organization. In this context, however, companies are faced with the challenge of having to rethink their structure. Because in order to be able to use data as a strategic resource, a digital value creation strategy must be developed. This should be aligned with the corporate strategy. Since different data is relevant for every company, the data strategies can also differ. Due to the type of data, the structure of the company, the product portfolio or the necessary hardware and software, big data projects of companies can ultimately look completely different.

Oracle has identified important components of a big data strategy  , which we will present to you below:

management

Acquire and capture data from the right sources. Then harmonize them and save them in the correct format. In this way, the data can be categorized and classified. The first goal is to guarantee the storage and retrieval of the data. However, the importance of data management goes beyond that. This becomes particularly clear as soon as the topic of big data comes into focus. The integration of data from different databases with different formats presents companies with a complex challenge.

analysis

The right tools help to find hidden knowledge. But without the necessary human background knowledge, a meaningful interpretation and intuition, these evaluations are initially of little use. For this reason, further training and study opportunities are increasingly being offered that deal specifically with data science as a potential career field.

integration

Data governance is about convergently controlling the management of access, usability, integration and security of data in an enterprise. In combination with suitable research & development platforms, analytics and visualization tools, the maximum benefit can be drawn from every byte.

application

Derive and implement measures from the knowledge gained. According to Oracle, this is the “home stretch” of a successful data strategy. This is how company-specific challenges can be solved. Whether marketing, production or purchasing – with the right applications, different areas of the company can access data and use it to their advantage. In the best case, employees from all areas of the company benefit from a comprehensive exchange.

data culture

Data – one or the other only associates numbers and facts with this term. A process of reframing should therefore take place in the design of a data culture . This means that data is understood as relevant information, if not knowledge. This might demystify the abstract notion. “A data-driven culture is a combination of process, people and technology that allows organizations to positively integrate understanding of data into everyday language.” says Ashish Thusoo, founder of Quoble, on the O’Reilly Radar podcast “Building a Data-Driven Culture” .

Cognizant identified 6 steps for a successful path to data culture :

  1. Development of a data supply chain
  2. Raising awareness of the diverse possibilities of data – “Art of the Possible”
  3. Transparency regarding the data obtained
  4. Communication of the successful implementation & advantages of data science in the company
  5. Identification of possible obstacles within the organization
  6. Open and comprehensive exchange to promote strategy and innovation

Conclusion

  • Big data is promising
  • Big data strategy and relevance & decision making based on data do not necessarily go hand in hand
  • In an international comparison, German companies have some catching up to do
  • Data quality forms an important basis
  • Data analytics are becoming more important across industries
  • Customer analysis is the most important area of ​​application
  • Data security, data quality and budget as a hurdle
  • Small and medium-sized companies with partly missing concepts
  • Data culture & data analytics plan as important levers

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