Absolute Business Impacts with Last Mile Analytics
Enterprises consider “last mile analytics” as the most business-critical data analytics process. The ‘last mile analytics’ is considered as the ‘bridging gap’ between the data analytics results/output/answers of business operations and how much, in reality, these results/outputs/answers have been implemented by businesses and its resources in real life for increased business outcomes.
Why should enterprises address the ‘last mile analytics’?
Following are the crucial ‘bridging gaps’ between the analytics output and reality outputs because of which Enterprises should vitally address the ‘last mile’ in analytics:
1.Utility to frontline Staff: Any type of data analytics, or AI that an enterprise spends so much on, and implements, would become a disaster if it is not being used primarily and regularly by the frontline staff. “Last mile analytics”, especially applies to the field staff and their business behavior in enterprises, across industry verticals. It is because they are the ones who, on an everyday basis majorly undertake the most impactful, critical business decisions
2.Justify core business objective: The Secondly, it is of utmost importance that insights being created for every staff through the ‘last analytical mile’ implementations are in line with the organization’s larger business objective
3.Patronage of Senior management: Unless the senior management’s beliefs totally align with the ‘last analytical mile’ implementation for every employee’s insights, they would not fulfill the purpose
4.Imbibing into organizational culture: Further, Enterprises must ensure that they imbibe necessary work cultural change at every employee level. It is because if analytics implementation fails at one operational level, the subsequent implementation levels are bound to fail as well
Should it be called ‘last’ mile then?
Well, no, it critically becomes the ‘first’ mile of analytics!!!
On one hand, today’s business intelligence and data analytics technologies churn-out insightful data for critical decision making. On the other hand, whether employees actually use these analytics results and act on them, will decide on whether they will bring out impactful, customer, and business outcomes.
Therefore, in terms of positive business outcomes, the moment during which Users practically work together with data analytics and its technology intelligence to bring out genuine business outcomes, is considered as the ‘last business mile’ or ‘last mile analytics’. And hence, it is extremely important for enterprises to address the ‘last mile’ challenge.
The practical scenario
Theoretically, ‘last mile analytics’ sounds like, businesses have so far made it perfectly gelled into their operational processes and workflows for effective, highly profitable business outcomes. However, practically, sadly, and historically speaking this has predominantly remained a mere assumption and far from the truth.
In fact, several experts in the business world have even expressed apprehension for terming it as ‘the last mile’. Why term it ‘last’ instead of making it the ‘focal point’, when it has the critical capability to drive positive project outcomes?
Why is ‘last mile analytics’ so crucial according to McKinsey?
The ‘last mile analytics’ holds the key for sure-shot successful business outcomes, if correctly absorbed right from the kick-start or planning stage.
The latest McKinsey report has highlighted nine crucial propellers of data analytics and business intelligence-driven, enterprise success. These are counted among the best business practices that enable any company to stand out in the apex, among all its competitors.
The ‘last mile analytics’ was clearly the major best practice highlighted and recommended by McKinsey. They have clearly demarcated the empirical supremacy of enterprises who spend over half their resources and funds on ‘last mile analytics’, in comparison to their competitors who don’t.
Speaking ‘last mile analytics’ in real-time
Numerous examples of implementing ‘last mile analytics’ to the doorstep use of every employee can be provided:
- In the banking and financial sector scenario, organizations can use the purchase information of its customers. This information can be provided to the sales team as cross-sales input for targeting potential customers for their credit cards
- Purchase Managers in retail chains can fetch sales data of fast-moving, slow-moving, and moderately moving items from individual retail outlets, for undertaking crucial purchase decisions
What ‘last mile’ facts enterprises should identify?
1.To aid operational staff in crucial decision making, what are the business processes that require complex algorithms to be built?
Devoting huge amounts of time, money, and resources in implementing high tech data analytics and business intelligence go waste if they are not carried into the place where they are actually effective, viz., in every employee’s business workflow.
Empowering every employee’s decision making is the key
Numerous enterprises have invested in tech-savvy digital analytics to bring out crucial business insights and build a suitable ‘last mile analytics’ business model. It is, however, every operational employee of an enterprise, who is the actual user of these analytics-driven insights. Where many businesses have failed, is in empowering every single staff to rightly catch/use these insights to bring out their ‘positively changed behaviors’ and real business throughputs.
Hence, in bridging this ‘last mile’, businesses should bring in every one of their field and front-line staff as the focal End-Users of data analytics insights, at the least on an everyday basis.
2.What are the operational areas where staff require seamless User Interface and experiences have to be built?
Last but not the least, the most productive data analytics implementation will be the one that is driven through its sheer User experience.
- It is its simplicity and intuitiveness that will attract the User to delightfully use data analytics
- Data analytics and business intelligence algorithms must be easy and insightful. They must be brought into the minute workflow operations that the end-user staff can easily understand without a sense of disruption
- This will motivate users to take a keen interest in them and make use of its advantages, which, will shape the enterprise’s analytics drive
- This, in turn, will act as the bridge for the ‘last mile’, providing Users with seamless analytics experience, driving towards overall business productivity
The famous statement by Forrester Analysts highlights these points, “Intelligent machines can help employees by taking routine and annoying tasks off their plates, delivering insights at key moments and freeing up time for them to focus on more interesting and valuable work.”
3.Is the analytics system being built to justify the organization’s core business objective?
Care has to be taken to retrospect at every step of analytics implementation, whether the larger organizational objectives were achieved. Because success comes with every step. Implementations have to be planned with metrics calculation at every staff level to compare with the organizational goals.
4.Does the Senior management fully agree with the analytics system being built at every operational level?
It is of vital importance that analytics implementors work in line with the total agreement of strategic corporate and technical stakeholders. It is mandatory to obtain a sign-off from the senior management if the analytics implementation is to their satisfaction at every micro-level staff operation.
5.Has the analytics system at every operational level brought out the desired positive changes in the organizational culture?
Last but not the least, every analytical exercise should entirely fulfill its corresponding purpose. It has to bring about the exact changes and adoption within organizational culture. Otherwise even if one or a few of the analytics implementation fail, the subsequent efforts may be undetermined.
Lastly, any successful analytics implementation is marked by an emergence of positive cultural change within every operational level of the Enterprise. Analytics developers have to plan implementations in such a way that every micro-level analytical operation brings in a positive attitude and work culture change. Otherwise, even if a single implementation brings out negativity, the subsequent ones are bound to fail.
How should enterprises address the ‘last mile’ challenge?
- Making things easy and quick for knowledge workers to fetch answers for their simple data queries. This can be done by building search interfaces similar to the workings of popularly accepted search engines such as Google
- Leverage on AI tools to help users to generate “suggest data queries”. These data query suggestions can be based on
- Organization’s core data movements, tendencies, and outliers
- Individual job nature of the User/knowledge worker of the data analytics application.
How to bridge last mile challenge - step-by-step approach
Enterprises should implement their ‘last mile’ using the following benchmarks as mandatory guidelines:
- Business component: Change and adoption in organization culture is key for success, without this culture, if few experiments fail, subsequent efforts may be undermined
- Complete alignment on the problem statement with all stakeholders – current state, desired state after solving the problem, gaps/limitations. Ensuring analytical outcomes is in line with the organization’s larger business objectives. Apply first-principle based approach to:
- Identify all internal and external factors and sub-factors influencing the problem statement
- For each factor, prepare an exhaustive hypotheses matrix
- For each hypothesis, identify the required data sources
- Complete the hypotheses matrix grid with
- Data is available internally
- Data can be procured externally
- Assumptions can be made
- Some hypotheses cannot be tested for lack of data
- Technology component : Create an ecosystem for developing and operationalizing model management, ability to rapidly combine, deploy, and maintain existing algorithms are key here factors for measuring success
- Create a centralized model repository: Data literacy, governance, lineage and catalogue are critical factors, ensuring knowledge transition and good overlap exists between IT, DevOps, Data Engineers and Data scientists
- Deploy analytical models into production: Measure and combine open source and commercial models for model selection and deployment into batch OS (ex: database, Spark), On-demand hosting (ex: Application, Cloud, real-time using streaming data)
- Performance monitoring and effectiveness: Trace and evaluate models periodically based on business impact and additional requirements and make decisions on Rebuild, Replace, Rearchitect, Revise, and Rehost.
Ammex digital implementation experts opine that it is high time enterprises start listing the ‘last mile analytics’ as one among their topmost priorities. It is the ‘last mile analytics’ which will ultimately have the ‘last say’ in the overall productivity and performance of an organization. To know more write to us at firstname.lastname@example.org.