Along with each new invention come its side effects or new challenges. This is true even in the case of data capturing, harnessing. Data is a holy grail for data scientists and organizations as it can help them reach the highest pinnacles of productivity, innovation, growth etc., but it comes with great responsibility. The organizations have to proactively prepare themselves in the domain of data policies, data security, legal issues, Technology, Organizational change and Talent, access to data etc. to successfully leverage the potential of data.
Data policies: As organizations start capturing and analyzing larger amounts of data, they need to setup policies that adhere and respect issues around cross national flow of data, intellectual property, and liability. Data can easily flow across the international borders in data pipes and the country or origination could be different from the country of analysis. This needs to moderated and there are policies that restrict such wide transfers of data for specific types of data like heath information. Also, there needs to be policies around who can analyze some sensitive data for individuals. So, policies restricting the use of data like credit score, SSN etc. are important for privacy considerations and preventing misuse of sensitive data. The increasing concerns around privacy of consumer data have been led by policies of some firms that have used consumerâs data for their own benefits. This needs to be mitigated by policies for protection of use of consumer data esp health and financial. Thus there is a tradeoff between utility and privacy that needs to be resolved.
Data Security: There are concerns around the security of data. Once there are policies to manage who has access and how much, we need to make sure that those policies are adhered to. In the recent past, there have been increasing instances of breach of consumer data by hackers and ill minded organizations. This has led to panic and concerns about security of data. As more and more consumer, organizational and national data gets digitalized; it would become important to protect that data with better technology and policies.
Legal Issues: Issues around use of data, ownership of data and liability arising from the use of data are new and would need to be understood and resolved. Data is different from other assets and can be easily transferred, copied and manipulated. So, this can lead to ownership issues that can become very important in a competitive situation, both within and across the organizations. There could be other issues related with the liability arising from the use and analysis of data, esp. incorrect analysis or implementation. This could have severe impact on the organization and would need clarification probably over time, to capture the full potential of data.
Technology and techniques: Need for data capture and analysis have brought organizations to a point where it is important to merge and use various data systems and mart to harness the complete value of that data. So, new techniques and technologies need to be employed to achieve this goal. Organizations need to develop the basic infrastructure and capability to support data capture, data integration, data analysis and reporting. This also implies that you need to invest in new technology, upgrade legacy systems and do change management to train personnel. There is also a need for new technologies that can help satisfy the need for data maneuvering and consumption in an easier fashion.
Organizational change and talent: This is a difficult issue and has many aspects to it. On one side, leadership may lack the understanding of big data and its potential benefits, so as to promote and approve initiatives to build capabilities. On the other side, there might be a lack to talent in the organization to effectively handle data and analyze it. This can be a big competitive advantage for companies that can use this data to effectively succeed in the market. Another issue is the lack of organizational structure, incentives to optimize the use of data to make better and informed decisions. So, the organizations have to take three fold actions â educate the leadership on the importance of big data and get their support; develop in-house capability or hire people that can handle big data; and create organizational structures to promote and optimize the use of data.
Access to data: The power of data multifold when it is integrated with other data sources to bring to light interesting insights. In most organizations, different departments use different systems with little scope for data integration. Also, as already stated, data ownership can provide the feeling of power and competitive advantage to some people in the organizations, leading to reluctance in sharing it and optimizing its use. So, we need to make sure that economic incentives are aligned within an organization to make the most effective use of data by sharing and integrating. To transform an organization, you may also need data from third party sources, and that might not be very easy to access and use. New business models are evolving and are being considered by different organizations to make such transactions easy.
Industry structure: Some industry structures have not evolved to imbibe the basic principles of efficiency and productivity. These industries are not impacted by competitive pressures and have a different rate of use of data. For example â government as well as health care are such industries where performance transparency is low and where data has not made much inroads. These industries need to improvise their productivity by using data more intensively to make more informed decisions. Organization leaders would have to determine how to evolve the structure of these organizations in an increasingly integrated and competitive world and how to use data to achieve and optimize them.
Thus, data as a business driver can be transformative for organizations if the above listed challenges can be tackled and the power of data is realized and utilized. All the stakeholders involved from leadership, to data scientists to policy makers need to understand the growing challenges as the data evolves and proactively counter them, so that we can create a culture that promotes and appreciates the use of data for everyoneâs benefits.