{"id":335,"date":"2020-03-07T07:50:44","date_gmt":"2020-03-07T06:50:44","guid":{"rendered":"https:\/\/digitaleconomy.org\/?p=335"},"modified":"2020-03-09T16:47:04","modified_gmt":"2020-03-09T15:47:04","slug":"developing-a-clear-data-strategy-for-an-effective-use-of-data","status":"publish","type":"post","link":"https:\/\/fromdatatoimpact.com\/index.php\/2020\/03\/07\/developing-a-clear-data-strategy-for-an-effective-use-of-data\/","title":{"rendered":"Developing a Clear Data Strategy"},"content":{"rendered":"\n

Key Aspects to Consider for an Effective Use of Data <\/em><\/p>\n\n\n\n

Wahab Moradi & Leonie Gehrmann<\/a><\/h4>\n\n\n\n
<\/div>\n\n\n\n

Nowadays, developments such as the Internet of Things (IoT), user-generated content, and the tracking and connectivity of devices have revolutionized the world of data. What is popularly referred to as big data is characterized by the three V\u2019s: volume, variety, and velocity (Tonidandel, King and Cortina, 2016). These large data sets bear great potential. The sheer volume can be leveraged as a competitive advantage, assuming more data provides more accurate insights on customers and the market environment (Lycett, 2013). With an understanding of which data can be discarded, firms are able to speed up data processing, generating information faster and allowing for quicker decisions and actions (Lycett, 2013). And while traditional data sets are typically structured, big data is generally obtained from a number of different sources, resulting in unstructured data sets with varying data types (Tonidandel, King and Cortina, 2016). The practice of using data to predict scenarios, trends, or even changes in customer needs is not novel, internal transaction data is often used for decision-making or forecasting. However, big data changes the requirements and tasks of companies working with data. Instead of merely falling prey to the hype, a deliberate use of (big) data should be the focus.<\/p>\n\n\n\n

Leading companies recognize that data is a strategic asset that needs to be gathered, adjusted, and analyzed to gain meaningful and actionable insights (Lukosius and Hymann, 2019). If successfully managed, the strategic use of data can result in a competitive advantage. However, this requires the development of a clear, consistent, company-wide and well-designed data strategy. Companies must ask themselves what the intended goal and scope of the data strategy is and consider the tasks of data collection, storage, and processing, as well as the potential insights and generated value (Erevelles, Fukawa and Swayne, 2015). For example, marketing departments stand to benefit from a strategic use of (big) data by analyzing market data und consumer behavior to generate measurable value (Kumar et al., 2013). In general, there is no single universally applicable data strategy, but this post provides first key components to consider.<\/p>\n\n\n\n

\nScope,\npurpose and mindset<\/strong><\/h2>\n\n\n\n

First,\nthe data strategy should reflect the company’s mission and vision\nregarding data handling, ensuring that the data strategy has an\nidentity and role within the company and is in line with the overall\nstrategic goals (Thompson et al. 1999). Only once clear objectives\nfor the data strategy have been defined, can the scope of the\nnecessary and relevant activities and data sources be considered.\nThereby, a reflection of the potential use of each instance should\nensure that the company\u2019s resources are used efficiently.\nFurthermore, the definition of key performance indicators that\nmeasure the success of the data strategy enables the discovery of\noptimization opportunities (Thompson et al. 1999).<\/p>\n\n\n\n

So\nfar, statistical models that make assumptions about the underlying\nrelationships between variables have been used in data-driven\ndecision-making processes (Breiman, 2001). However, the unfeasibility\nof processing unstructured data sets via predefined stochastic models\nrequires a change in mentality and culture, a great challenge for\nmany companies. When defining the scope and purpose of the data\nstrategy, managers must also assess how data-driven the corporate\nstrategy and employees are. Since analysis tools for (big) data are\nunder constant development, flexibility and eagerness to learn are\nrequired.<\/p>\n\n\n\n

Data\ncollection, storing and processing<\/strong><\/h2>\n\n\n\n

These\ngeneral considerations guide each of the following steps in the use\nof data. The collection and storage is particularly important to\nensure sufficient data quality for further processing (Simsek et al.,\n2019). However, especially the volume of unstructured big data\nrenders traditional hardware and software solutions inadequate to\nacquire and store the necessary data, requiring an investment in new\nsolutions (Bharadwaj et al., 2013). For example, very large and\nunstructured data bits could be pre-processed and stored in an\naggregated manner. In addition to ensuring that the data collected\nfits the scope and objectives of the data strategy, it also must be\ncleaned before storage, thereby removing redundant or meaningless\ninformation from the data set (Lycett, 2013).<\/p>\n\n\n\n

Beyond\nan adequate IT solution, companies must also consider what type of\ninfrastructure to use for the collection and storage of data. This\ndecision depends on the future uses of the data. Legal regulations of\ndata privacy must also be respected and employee access rights to\ndata within the company must be reflected upon. Weinberger (2007)\ndiffers between a clearly structured data architecture or “sorting\nin the way in” type and an unstructured data architecture or\n“sorting in the way out” type. For the first type, data is\nsorted and its location determined definitively once it enters the\nsystem or data infrastructure (Constantiou and Kallinikos, 2015). For\nthe second type, unstructured data and collected or generated\ninformation are sensibly categorized and compiled and can be used for\ndifferent purposes in the future (Constantiou and Kallinikos, 2015).<\/p>\n\n\n\n

Similarly,\ndata lakes, large data sets stored company-wide in their native\nformat, are another possibility for data storage (Kitchens et al.,\n2018). The elimination of barriers and preliminary costs for data\nexchange within the company are some of its advantages (Kitchens et\nal., 2018). However, the lack of standardization and integration of\nthe data requires relevant data to be fished out of the lake,\nreorganized, and assigned to the holistic customer view for each\nanalysis (Kitchens et al., 2018). In the worst case, if the data has\nnot been collected appropriately to allow a link to other data\nsources, it might not be useful at all (Kitchens et al., 2018). \n<\/p>\n\n\n\n

Data\nprocessing leads to the extraction of insights. Taking a look at the\nmarketing department for example, individually collected and stored\ndata bits themselves do not provide high value for companies, since\nthey contain too little information as a single data element. Only\nthrough processing and aggregation can the separate data elements be\nplaced into the correct context and consumer behavior patterns\nrecognized (Chen, Chiang and Storey, 2012). Since external data is\nnot exclusive to a single company and can also be analyzed by\ncompetitors, a combination of internal and external data is likely to\nprovide more sophisticated and unique insights (Grover et al., 2018).<\/p>\n\n\n\n

The\nnature of unstructured data provides a new challenge for data\nprocessing since now text, image, and sound must be analyzed (Hu et\nal., 2019). Furthermore, when processing large data sets, less\ntraditional models, software and hardware can be applied and more\nalternative methods such as artificial intelligence (AI) and machine\nlearning (ML) are necessary and can provide real-time analyses\n(Adamopoulos, Ghose and Todri, 2018). However, to build up the\nrequired competencies, not only financial resources but also notably\nhuman capital are needed. According to a Bain & Company survey,\n56% of executives report their company lacks the skills to develop\ndeep data-driven processes (Grover et al., 2018). So when designing\nthe data analytic processes, managers need to define what insights\nare necessary and what procedures, products, and services should be\nimproved. A review of the existing and potential assets and\ncapabilities will determine whether sufficient human capital lies in\nthe company or must be purchased externally.<\/p>\n\n\n\n

Generating\ninsights and creating value <\/strong>\n<\/h2>\n\n\n\n

In\ngeneral, there are an abundance of opportunities for firms to create\nvalue using big data. Focusing only on marketing instruments,\ninsights of a clear data strategy and the associated development of\nprocesses and structures for the utilization of data can create added\nvalue for customers in many ways. For example, products and services\ncan be improved, customer experience can be enhanced, and internal\nprocesses can be optimized to improve response speed to customer\ninquiries (Grover et al., 2018).<\/p>\n\n\n\n

Companies\nsuch as Netflix, Spotify, and Uber have revolutionized their\nindustries by using big data to innovate products and services and\ngenerate a competitive advantage. Their ability to use data more\nefficiently and effectively for innovation processes than traditional\ncompetitors is related to their structure, culture, and internal\nprocesses (Troilo, De Luca and Guenzi, 2017). These companies\nminimize the uncertainty and risk associated with innovation by\nproactively using big data to analyze customer needs and feedback in\nthe form of consumption patterns or social media postings to support\nthe market success of new products or identify potential defects.\nFurthermore, big data opens the path to entirely new business models\nsuch as is the case for the insurance industry and novel approaches\nof the measurement and collection of data. By incorporating the\ninformation from various sensors capturing things such as speed or\nbraking habits, companies can offer new insurance products dependent\non the individual driving behavior (Varian, 2010).<\/p>\n\n\n\n

More\ncompanies recognize the importance of customer-centricity. It is\ndifficult for companies to differentiate themselves significantly\nfrom their competitors in a connected and highly transparent world.\nTherefore, marketing departments in particular are called upon to\noffer a fulfilling customer experience (Troilo, De Luca and Guenzi,\n2017). Insights extracted from big data can aide in the analysis of\ncustomer touchpoints, determining their amount and quality, as well\nas necessary improvements and where customers do (not) wish to\nencounter them (Troilo, De Luca and Guenzi, 2017).<\/p>\n\n\n\n

Similarly,\nnowadays customers can be divided into much smaller and more detailed\nsegments based on preferences, characteristics, and personality,\nallowing companies to meet the trend of consumers\u2019 desired\npersonalization. Recommender systems are exemplary value-generating\npersonalization services, suggesting products or services that match\nthe customer\u2019s preferences or past purchases. Additionally,\npersonalized advertising lets customers feel directly addressed and\nconsidered, strengthening their relationship with the company\n(Varian, 2010).<\/p>\n\n\n\n

Discussion<\/strong><\/h2>\n\n\n\n

While\nclear potentials of big data for value creation were mentioned, it is\nimportant to keep in mind that a blind jump into the vastness of big\ndata analytics is likely to be unsuccessful. Rather, companies must\nconduct research on AI applications in order to analyze the large\namount of different data types in a reasonable timeframe to draw\nmeaningful insights. There still exists potential in the development\nof methods e.g. to analyze non-textual data. Additionally, novel\ncollection and storage solutions must be determined with fewer\ndisadvantages and more regard for data governance and security topics\nthan initial approaches. Current trends such as autonomous vehicles,\nincreasingly connected customers, and IoT will further multiply the\namount of generated data. Furthermore, the use of big data requires\nethical considerations and companies to reflect on the ensuing\nprivacy restriction of consumers and societal transparency.<\/p>\n\n\n\n

As\nindicated, there is no universally applicable data strategy. Managers\nwishing to implement an effective use of (big) data in their company\nmust consider each of the mentioned components when designing an\napproach tailored to their company, industry, and goals. Honoring an\nefficient and effective use of a company\u2019s finite resources, a\nsuccessful data strategy collects and stores meaningful data while\ndisregarding meaningless data. Furthermore, the rise of big data\nexternal to the company does not diminish the importance of internal\ndata generated through interactions among employees, customers, and\nsuppliers and available only to the company. In fact, an appropriate\nmix of external and internal data is another key to success and the\nbasic considerations mentioned here generally apply to all\ndata-driven activities in a company \u2013 regardless of the size of the\ndata set.<\/p>\n\n\n\n

Concluding, managers face three main important challenges in the development of a clear data strategy. First, the corporate culture must reflect a data-driven and customer-centric way of thinking and an internalization of the importance of big data. Second, companies must find talented human capital specializing in data analytics and AI. Third, increasingly stringent data protection and security regulations impact companies\u2019 data collection and storage processes and must be considered in the strategy development process. <\/p>\n\n\n\n

<\/div>\n\n\n
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<\/span>References<\/div>
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