Innovative disruptions usually have a three pronged effect. They are cheaper for customers, more accessible for usage or distribution purposes and leverage a business model with structural cost benefits. A lot of business innovations do not initially boast of any or all of these three advantages, but most eventually do catch up on closer introspection. Uber only became scalable once Uber-X could attach itself with multiple drivers or vehicles. Android smartphones were more expensive than simple Nokia phones as they had greater features, but eventually disrupted the existing market due to the several apps involved which afforded a more wholesome experience to the customer. It is Big Data that is allowing all such disruptive firms to streamline their processes. Thus in the face of disruptive competition, existing players can survive or even thrive if they assess how to face these new threats and come up with suitable solutions.

source:https://hbr.org/2016/01/how-big-data-is-changing-disruptive-innovation

The function of business analytics is to process data to derive meaningful insights for organizations Yet in practice, organizations are spending far too long these days in trying to capture the data rather than making it work. Five major approaches have been approached using which companies can better track data to help their employees add value. First of all, data must be used to confirm decisions rather than the latter being developed post analysis of the former. The design of such tools or platforms which track data must be done in accordance with the people expected to use the same. This includes frontline staff such as sales representatives, financial advisers, retail category managers and wealth managers. The purpose of the data must be to empower the staff in decision making, not to eliminate them through automation. This analytical capacity being developed must be done keeping in mind the entire organization and not any one department or branch. Over analysis leads to paralysis thus extracting key business intelligencemust be the aim not frivolous metrics. Thus distractions must be removed but instead focus must be on metrics that really matter to the organization. Source: https://hbr.org/2016/08/using-data-to-strengthen-your-connections-to-customers

Management publications are awash with articles on Big Data. Yet very few companies know how to use it properly. Data warehousing on its own is redundant if company does not follow a few basic principles. The first of those is that over-analysis may paralyze essential business tasks. All decision making post this analysis must be based around the creation of value rather than cost alone. Data must help marketers to personalize content for various campaigns. Only such data must be measured on which some action may be taken. It is also important to understand when the data must be used. Sometimes it can be used for campaign analysis on others to scrutinize customer segments. Marketing channels must be optimized using this huge chunk of data Specific messaging strategies can then be targeted using various methods such as email, phone or website. Most importantly, analysis of data adds great depth to customer engagement. The customer can be understood much better as a result of so and content can be personalized for individual personas.

Source: http://www.forbes.com/sites/theyec/2016/08/01/make-big-data-your-marketing-advantage/#7891ce326245

There are some common mistakes that most managers commit with the use of business analytics. First of all many do not understand that Big Data on its own is not as useful as its proper understanding and integration with various sources of data to get the complete holistic picture. Having too much of data is also not useful specially when it is in unstructured form. A lot of managers read too much into correlations, even though in reality a lot of them maybe simply coincidences. Machine learning algorithms do develop certain trends which appear to lead to business outcomes but actually spurious findings. More than the data itself, it is its handling that is crucial and thus managers must assemble a good team to work on the data. The right analyst and the right skill level will deduce authentic results out of the data.

Source: https://hbr.org/2016/07/the-4-mistakes-most-managers-make-with-analytics

There are several business these days that are not leveraging the kind of business insights they are obtaining. Insights driven organizations such as Uber, Google, Facebook, Netflix and Amazon are disrupting traditional business. These kind of organizations leverage business analytics to address more informed decision making. Strategic investment is done in analytics with involvement of the top management. These companies are agile and operate in a close loop thus ensuring that their implementation of observations is much faster. They realize that humans are ultimately smarter than machines and thus do not put blind faith on the latter. Before devising algorithms to capture insights, they consult the people with technical knowledge in those domains concerned. Enormous amount of data warehousing is done to conduct the analysis.

Source: http://www.forbes.com/sites/forrester/2016/07/29/insights-driven-business-are-stealing-your-customers/#530d5c07410a

Over the last few years, customers’ perception towards their relationship with companies has evolved. Earlier, any form of intimacy shown by the sellers was seen as akin to stalking, but now customers have also realized that with the vast amount of data available, companies are bound to track that for business purposes. They also understand that eventually this can led to a better level of service. One company SAS, used business analytics effectively to redraw the conventional customer journey. At first level, a need arises. Then some form of research goes in to searching for the top players and options. Then, decision is made to choose one brand over any of its competitors. The purchase then takes place followed by usage of the product. After satisfaction, the customer becomes a brand advocate and starts recommending the same to others as well.

Source: https://hbr.org/2016/08/how-one-company-used-data-to-rethink-the-customer-journey

IT, analytics and operations are departments within organizations which need to work together. The models of collaboration may be different but the end objective has to be efficient delivery of services. Four models have been revealed. In the first one, operational data and business analytics functions are integrated to one team. Here no corporate IT is needed, instead processes are inbuilt. Then there is the model where analytics and IT are independent of each other. Here it is corporate IT that is used as the engine to power analytics. In the third model, data analytics is embedded within IT. This model avoids digital disruptions but business demands exceed the capacity very often. On the other hand, the last model has the same analytics embedded within operations department. All these models have some advantages or disadvantages over the other ones.

Source: https://hbr.org/2016/08/figuring-out-how-it-analytics-and-operations-should-work-together